This paper considers energy management on LTE-enabled Internet of Things (IoT) devices. A characteristic feature of IoT applications is the periodic generation of small messages, whose transmission over LTE is highly energy inefficient. In this paper, we consider application message bundling to alleviate the effect of short message transmissions on energy consumption. Specifically, we model the interplay between energy consumption and the extended DRX mechanism introduced in LTE to deal with IoT traffic. We formulate bundling as a cost minimization problem and develop an online algorithm to solve the problem. Detailed analysis shows that, depending on DRX and application parameters, our algorithm is 1, 2, or 4-competitive with respect to the optimal offline algorithm that knows the entire sequence of application messages a priori. We evaluate the performance of the proposed algorithm and the accuracy of our analysis in a range of realistic scenarios using both model-driven simulations and real experiments on an IoT testbed. Our results show that, i) depending on application requirements, energy savings ranging from zero to about 100% can be achieved using our algorithm, and ii) ignoring DRX could significantly overestimate or underestimate energy consumption.

To reduce the energy consumption of a smartphone, multiple data transfer requests from applications can be bundled together and granted at once in order to reduce the time the radio interface is on. The side effect of bundling is the increased delay experienced by mobile applications. While several bundling algorithms have been proposed in the literature, a general and systematic solution to balance the energy-delay tradeoff is missing. In this paper, we formulate bundling as a cost minimization problem, in which the tradeoff between energy and delay is captured by a cost function. We then propose an online algorithm for minimizing the bundling cost and show that the algorithm is 4-competitive with respect to the optimal offline algorithm that knows the entire sequence of data transfer requests a priori. We evaluate the performance of the proposed algorithm and the accuracy of our results in a range of realistic scenarios using both model-driven simulations and real experiments on a smartphone. Our results show that depending on the delay tolerance level of a user, energy savings ranging from zero (delay intolerant) to about 100% (delay tolerant) can be achieved using our algorithm.

To load a webpage, a web browser first downloads the base HTML file of the page in order to discover the list of objects referenced in the page. This process takes roughly one round-trip time and constitutes a significant portion of the web browsing delay on mobile devices as wireless networks suffer from longer transmission and access delays compared to wired networks. In this work, we propose a solution for eliminating this initial delay, which is transparent to end systems, does not require modifying HTTP, and is well suited for web browsing on mobile devices. Our solution, called WebPro, relies on a network proxy that builds an up-to-date database of resource lists for the websites visited frequently by network users. The proxy resides in the wired part of the network, and hence can afford to pro-actively build and refresh the resource list database periodically. When a request for a webpage comes to the proxy, it simultaneously fetches the base HTML and all referenced objects required to render the webpage using the corresponding resource list stored in the local database. We have built a working prototype of WebPro and have conducted live experiments over WiFi and LTE networks. Our results show an average of 26% reduction in page load time for a mix of popular web sites chosen from categories such as news, sports and shopping. Moreover, in comparison to another best known proxy-based solution, WebPro provides delay reductions ranging from 5% to 51% for a variety of web sites.

In recent years, there has been an increasing demand for ubiquitous streaming like applications in data networks. In this paper, we concentrate on NUM-based rate allocation for streaming applications with the so-called S-curve utility functions. Due to non-concavity of such utility functions, the underlying NUM problem would be non-convex for which dual methods might become quite useless. To tackle the non-convex problem, using elementary techniques we make the utility of the network concave, however this results in reverse-convex constraints which make the problem non-convex. To deal with such a transformed NUM, we leverage Sequential Convex Programming (SCP) approach to approximate the non-convex problem by a series of convex ones. Based on this approach, we propose a distributed rate allocation algorithm which under mild conditions converges to a locally optimal solution of the original NUM. Numerical results validate the effectiveness, in terms of tractable convergence of the proposed rate allocation algorithm.