Modern mobile devices are equipped with multicore-based processors, which introduce new challenges on computation offloading. With the big.LITTLE architecture, instead of only deciding locally or remotely running a task in the traditional architecture, we have to consider how to exploit the new architecture to minimize energy while satisfying application completion time constraints. In this paper, we address the problem of energy-efficient computation offloading on multicore-based mobile devices running multiple applications. We first formalize the problem as a mixed-integer nonlinear programming problem that is NP-hard, and then propose a novel heuristic algorithm to jointly solve the offloading decision and task scheduling problems. The basic idea is to prioritize tasks from different applications to make sure that both application time constraints and task-dependency requirements are satisfied. To find a better schedule while reducing the schedule searching overhead, we propose a critical path based solution which recursively checks the tasks and moves tasks to the right CPU cores to save energy. Simulation and experimental results show that our offloading algorithm can significantly reduce the energy consumption of mobile devices while satisfying the application completion time constraints.

Computation offloading has been widely used to alleviate the performance and energy limitations of smartphones by sending computationally intensive applications to the cloud. However, mobile devices with poor cellular service quality may incur high communication latency and high energy consumption for offloading, which will reduce the benefits of computation offloading. In this paper, we propose a peer-assisted computation offloading (PACO) framework to address this problem. In PACO, a client experiencing poor service quality can choose a neighbor with better service quality to be the offloading proxy. Through peer to peer interface such as WiFi direct, the client can offload computation tasks to the proxy which further transmits them to the cloud server through cellular networks. We propose algorithms to decide which tasks should be offloaded to minimize the energy consumption. We have implemented PACO on Android and have implemented three computationally intensive applications to evaluate its performance. Experimental results and simulation results show that PACO makes it possible for users with poor cellular service quality to benefit from computation offloading and PACO significantly reduces the delay and energy consumption compared to existing schemes.

Wearable devices such as smartwatches do not have enough power and computation capability to process computationally intensive tasks. One viable solution is to offload these tasks to the connected smartphone. Existing Android smartphones allocate CPU resources to a task according to its performance requirement, which is determined by the context of the task. However, due to lack of context information, smartphones cannot properly allocate resources to tasks offloaded from wearable devices. Allocating too few resources to urgent tasks (related to user interaction) may cause high interaction latency on wearable devices, while allocating too many resources to unimportant tasks (unrelated to user interaction) may lead to energy waste on the smartphone. To solve this problem, we propose a context-aware task offloading (CATO) framework, in which offloaded tasks can be properly executed on the smartphone or further offloaded to the cloud based on their context, aiming to achieve a balance between good user experience on wearable devices and energy saving on the smartphone. To validate our design, we have implemented CATO on the Android platform and developed two applications on top of it. Experimental results show that CATO can significantly reduce latency for urgent tasks and save energy for other unimportant tasks.

In-app advertising provides a monetization solution for free apps, but also consumes lots of energy due to the long tail problem in cellular networks. To reduce the tail energy, we can predict the number of ads needed in the future and then prefetch those ads together instead of periodically. However, prefetching unnecessary ads may waste both energy and cellular bandwidth, and this problem becomes worse when the network quality is poor. In this paper, we propose network quality aware prefetching algorithms. First, we design a prediction algorithm which generates a set of prefetching options with various probabilities. Second, with these prefetching options, we propose two prefetching algorithms to reduce the energy consumption by considering the effect of network quality, where the energy-aware prefetching algorithm aims to minimize the energy consumption, and the energy-and-data aware prefetching algorithm considers the data usage to achieve a tradeoff between energy and data usage. Evaluation results show that, compared to traditional ways of fetching ads periodically, our energy-aware prefetching algorithm can save 80% of energy under various network quality, and our energy-and-data aware prefetching algorithm can achieve similar energy saving with less data usage.

Computationally intensive applications may quickly drain mobile device batteries. One viable solution to address this problem utilizes computation offloading. The tradeoff is that computation offloading introduces additional communication, with a corresponding energy cost. Yet, previous research into computation offloading has failed to account for the special characteristics of cellular networks that impact mobile device energy consumption. In this paper, we aim to develop energy efficient computation offloading algorithms for cellular networks. We analyze the effects of the long tail problem on task offloading, formalize the computation offloading problem, and use Dijkstra's algorithm to find the optimal decision. Since this optimal solution relies on perfect knowledge of future tasks, we further propose an online algorithm for offloading. We have implemented this latter algorithm on Android-based smartphones. Both experimental results from this implementation and trace-driven simulation show that our algorithm can significantly reduce the energy of computation offloading in cellular networks.

Trust mechanism is a crucial part of the P2P applications, which limits their rapid development. Unfortunately, current mechanisms were unable to detect the malicious peers accurately under the disturbing of various malicious reports. This paper proposes a new trust model, named Similarity Transitivity chain based Trust Model (STTM), to evaluate peers' global trust more precisely. Firstly, we employ PeerStrategy to produce feedbacks. Then we offer a novel similarity transitivity chain, which is formed by the peers who have common neighbors, to calculate report trustworthiness. By considering the peer's feedbacks as a vector, we calculate the personal similarity of each pair that has common neighbors. After that, we transit such personal similarities through the chain and get the report trustworthiness vector when all the peers come to a static view. Finally, we make use of maximum likelihood estimation equation to compute global trust. The simulation results show that our trust model is able to estimate peers' global trust accurately in most scenarios, especially in the scenario of two collusion groups. Also, it sharply decreases the mean error of trust estimation and performs better than other trust models.