Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), a MEC network can be decomposed as a radio-access network cascaded with a CS network. Based on the architecture, we investigate network-constrained latency performance, namely communication latency and computation latency under the constraints of radio-access coverage and CS stability. To this end, a spatial random network is modelled featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Given the model and the said network constraints, we derive the scaling laws of communication latency and computation latency with respect to network-load parameters (density of mobiles and their taskgeneration rates) and network-resource parameters (bandwidth, density of APs/CSs, CS computation rate). Essentially, the analysis involves the interplay of theories of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latencies, network coverage and network stability. The results provide useful guidelines for MEC-network provisioning and planning by avoiding either of the cascaded radio-access network or CS network being a performance bottleneck. I. INTRODUCTIONOne key mission of 5G systems is to provide users ubiquitous computing services (e.g., multimedia processing, gaming and augmented reality) using servers at the network edge, called mobile edge computing (MEC) [1]. Compared with cloud computing, MEC can dramatically reduce latency by avoiding transmissions over the backhaul network, among many other advantages such as security and context awareness [2], [3]. Most existing work focuses on designing MEC techniques by merging two disciplines: wireless communications and mobile computing.In this work, we explore a different direction, namely the design of large-scale MEC networks with infinite nodes. To this end, a model of MEC network is constructed featuring spatial random distribution of network nodes, wireless transmissions, parallel computing at servers. Based on the model and under network performance constraints, the latencies for communication and

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PLA/PBAT blends and PLA/PBAT/MWNT nanocomposite systems were prepared via a melt mixing process to examine their thermal and rheological properties. To compare the polymer blend/MWNT nanocomposite with a pure polymer/MWNT nanocomposite, PLA/MWNT, PBAT/MWNT, and PLA/PBAT/MWNT nanocomposite systems were prepared. TEM and SEM were used to observe that one phase has better affinity with the MWNT, while the MWNT was found to increase both the thermal properties of the PLA/PBAT blends and rheological properties of the PLA/PBAT/MWNT nanocomposite with distinct shear‐thinning behavior due to the addition of the MWNT. An increase in the storage (G′) and loss (G″) moduli for the PLA/PBT/MWNT nanocomposite was also observed.magnified image

The conventional designs of mobile computation offloading fetch user-specific data to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching is proposed in this paper that seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetcheddata sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.

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Attaining the vision of Smart Cities requires the deployment of an enormous number of sensors for monitoring various conditions of the environment ranging from air quality to traffic. Backscatter sensors have emerged to be a promising solution for two reasons. First, transmissions by backscattering allow sensors to be powered wirelessly by radio-frequency (RF) waves, overcoming the difficulty in battery recharging for billions of sensors. Second, the simple backscatter hardware leads to low-cost sensors suitable for large-scale deployment. On the other hand, backscatter sensors with limited signalprocessing capabilities are unable to support conventional algorithms for multiple access and channel training. Thus, the key challenge in designing backscatter sensor networks is to enable readers to accurately detect sensing values given simple ALOHA random access, primitive transmission schemes, and no knowledge of channel states and statistics. We tackle this challenge by proposing the novel framework of backscatter sensing (BackSense) featuring random encoding at backscatter sensors and statistical inference at readers. Specifically, assuming the widely used on/off keying for backscatter transmissions, the practical random-encoding scheme causes the on/off transmission of a sensor to be randomized and follow a distribution parameterized by the sensing values. Facilitated by the scheme, statistical inference algorithms are designed to enable a reader to infer sensing values from randomized transmissions by multiple backscatter sensors. The specific design procedure involves the construction of Bayesian networks, namely deriving conditional distributions for relating unknown parameters and variables (including sensing values, noise power, sensing measurements, number of active sensors) to signals observed by the reader. Then based on the Bayesian networks and the well-known expectationmaximization (EM) principle, inference algorithms are derived to recover sensing values. Simulation of the BackSense system demonstrates high accuracy in reader inference despite the mentioned limitations of backscatter sensors, which grows with increasing numbers of received symbols and reader antennas.

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