Deep Neural Networks (DNNs) have been successfully used in a number of application areas. As the number of layers and neurons in DNNs increases rapidly, significant computational resources are needed to execute a DNN. This ever-increasing resource demand is currently met by large-scale data centers with state-of-the-art GPUs. However, increasing availability of mobile edge computing and 5G technologies provide new possibilities for DNN-driven AI applications, especially where these application make use of data sets that are distributed in different locations. One fundamental process of a DNN-driven application in mobile edge clouds is the adoption of ``inferencing'' - the process of executing a pre-trained DNN based on newly generated data from mobile devices. We investigate offloading DNN inference requests in a 5G-enabled mobile edge cloud (MEC), to admit as many inference requests as possible. We propose exact and approximate solutions for the problem. We also consider dynamic task offloading for inference requests, and devise an online algorithm that can be adapted in real time. The proposed algorithms are evaluated through large-scale simulations and using a real world test-bed. The experimental results demonstrate that the empirical performance of the proposed algorithms outperform their theoretical counterparts and other similar heuristics reported in literature.

Vehicular Content Centric Networks (VCCNs) emerge as a strong candidate to be deployed in informationrich applications of vehicular communications. Due to vehicles' mobility, it becomes rather inefficient to establish end-to-end connections in VCCNs. Consequently, content packets are usually sent back to the requesting node via different paths in VCCNs. To improve network performance of VCCNs, node mobility should be exploited for vehicles to serve as relays and to carry data for delivery. In this work, we propose a scheme called Cooperative Caching based on Mobility Prediction (CCMP) for VCCNs. The main idea of CCMP is to cache popular contents at a set of mobile nodes that may visit the same hot spot areas repeatedly. In our CCMP scheme, we use Prediction based on Partial Matching (PPM) to predict mobile nodes' probability of reaching different hot spot regions based on their past trajectories. Vehicles with longer sojourn time in a hot region can provide more services and should be preferred as caching nodes. To solve the problem of limited buffer at each node, we design a cache replacement based on content popularity to guarantee only popular contents are cached. We evaluate CCMP through the ONE simulator for its salient features in success ratio and content access delay compared to other state-of-the-art schemes.

Communications in vehicular ad-hoc network (VANET) are subject to performance degradation as results of channel fading and intermittent network connectivity. The emerging Vehicular Content Centric Network (VCCN) is promising in supporting the needs of contents and alleviating the communication problems in VANET. Specifically, to improve the cache hit ratio and reduce the access delay of content retrieval, it helps to choose the appropriate vehicles to cache the frequently accessed data items. In this paper, we propose a Cooperative Caching scheme based on Social Attributes and Mobility Prediction (CCSAMP) for VCCN. CCSAMP is based on the observation that vehicles move around and are liable to contact each other according to drivers' common interests or social similarities. A caching node sharing more social attributes with the content requester is more likely to be interested in the same contents and distribute the contents to others with similar interests. Furthermore, a caching node that frequently meets other nodes is a better candidate to keep cache copies. CCSAMP also exploits the regularity of vehicle moving behaviors to predict the chance for a vehicle to reach hot zones based on Hidden Markov Model (HMM). We evaluate CCSAMP through the ONE simulator to demonstrate its higher performance.

Stringent delay requirements of many mobile applications have led to the development of mobile edge clouds, to offer low latency network services at the network edges. Most conventional network services are implemented via hardware-based network functions, including firewalls and load balancers, to guarantee service security and performance. However, implementing hardware-based network functions usually incurs both a high capital expenditure (CAPEX) and operating expenditure (OPEX). Network Function Virtualization (NFV) exhibits a potential to reduce CAPEX and OPEX significantly, by deploying software-based network functions in virtual machines (VMs) on edge-clouds. We consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request has both service function chain and end-to-end delay requirements. Specifically, each multicast request requires chaining of a sequence of network functions (referred to as a service function chain) from a source to a set of destinations within specified end-to-end delay requirements. We devise an approximation algorithm with a provable approximation ratio for a single multicast request admission if its delay requirement is negligible; otherwise, we propose an efficient heuristic. Furthermore, we also consider admissions of a given set of the delay-aware NFV-enabled multicast requests, for which we devise an efficient heuristic such that the system throughput is maximized, while the implementation cost of admitted requests is minimized. We finally evaluate the performance of the proposed algorithms in a real test-bed, and experimental results show that our algorithms outperform other similar approaches reported in literature.

As a more energy-efficient WPT technology, directional WPT is applied to supply energy for wireless rechargeable sensor networks (WRSNs). Conventional methods that ignore anisotropic energy receiving property of rechargeable sensors cause a waste of energy. To address this issue, in this paper, we focus on minimizing the charging delay with a directional charging scheme. At first, we introduce linear constraints to improve an energy transfer model, which is verified to be practical by experiments. Then, we concern a Minimal chArging Delay with Single charger (S-MAD) problem to promote efficiency, followed by an Optimal Direction Charge with Single charger (S-ODC) solution. Through discretizing charging power and angle, we bound the performance gap of the solution to the optimal one with $\text{a}^{^{^{^{}}}}\,\,\frac {1}{1-\epsilon ^{2}}$ approximation ratio, where $\epsilon $ is the error threshold of discretization. After that, we extend the original S-MAD problem into the large scale WRSN with multiple chargers (i.e., M-MAD) and solve it by proposing M-ODC (i.e., Optimal Direction Charge with Multiple chargers (M-ODC)). Theoretical analyses are presented to exploit the feature of the proposed schemes. Finally, we demonstrate that our methods outperform the baseline methods by an average of 34.2% through simulations and test-bed experiments.

As an epoch-making technology, wireless power transfer incredibly achieves energy transmission wirelessly, enabling reliable energy supplement for Wireless Rechargeable Sensor Networks (WRSNs). Existing methods mainly concentrate on performance improvement theoretically, neglecting the fact that most Commercial Off-The-Shelf (COTS) rechargeable sensors ( e.g. , WISP and Powercast) are not allowed to conduct sensing and energy harvesting tasks simultaneously, termed charging exclusivity . Therefore, their schemes are not feasible for practical applications. In this paper, we focus on the charging exclusivity issue in stochastic events monitoring while improving network performance. In specific, we pay close attention to trading off charging and sensing tasks and formulate a combinatorial optimization problem with routing constraints. We introduce novel discretization techniques and investigate the routing problem to reformulate the original problem into maximization of a submodular function. With a slightly relaxed budget, the output of our proposed algorithm is better than $(1-1/e)/2$ of the optimal solution to the original problem with a smaller charging radius $(1-\xi)D_{c}$ . Through extensive simulations, numerical results show that in terms of charging utility, our algorithm outperforms baseline algorithms by 21.3% on average. Moreover, we conduct test-bed experiments to demonstrate the feasibility of our scheme in real scenarios.