In wireless ad hoc networks, due to the interference between concurrent transmissions, the per-node capacity generally decreases with the increasing number of nodes in the network. Caching can help improve the network capacity, as it shortens the content transmission distance and reduces the communication interference. However, current researches on the capacity of wireless ad hoc networks with caching generally assume that content popularity follows a uniform distribution. They ignore the fact that contents in reality have skewed popularity, which may lead to totally different capacity results. In this paper, we evaluate how the distribution of the content popularity affects the per-node capacity, and derive different capacity scaling laws based on the skewness of the content popularity. Our results suggest that for wireless networks with caching, when contents have skewed popularity, increasing the number of nodes monotonically increases the per-node capacity.

Mobile Social Network (MSN), built on inter-connected mobile devices, enables the flexibility of information exchanges of individuals within a virtual community. MSN may be self-organized and/or infrastructure-less, and the communication links may frequently fluctuate. When link qualities degrade, it remains critical to maintain the connections of important social pairs when supporting all social pairs is impossible. To achieve this goal, we propose to proactively place some reliable links (e.g., satellite links or UAV links) into the underlying communication network, so as to improve the service quality of the important social pairs, referred to as the Maintaining Social Connections (MSC) problem. We formulate the MSC problem and prove it is NP-hard. As such, we first study a special case of MSC, which is submodular and solvable with high approximation-ratio. Since the general MSC problem is not submodular, we propose an efficient approximation algorithm. Specially, by carefully choosing two submodular functions to lower/upper bound the MSC problem, we are able to prove the high approximation ratio of the proposed algorithm using these selected submodular functions. We further develop evolutionary algorithms to iteratively adjust the link placement via different exploration strategies, which also yield guaranteed approximation-ratio. Extensive evaluations based on both synthetic and real-world social network traces demonstrate the effectiveness of our proposed algorithms.

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 wireless ad hoc networks, due to the interference between concurrent transmissions, the per node capacity generally decreases with the increasing number of nodes in the network. Caching can help improve the network capacity, as it shortens the content transmission distance and reduces the communication interference. However, current researches on the capacity of wireless ad hoc networks with caching generally assume that content popularity follows uniform distribution. They ignore the fact that contents in reality have skewed popularity, which may lead to totally different capacity results. In this paper, we evaluate how the distribution of the content popularity affects the network capacity, and derive different capacity scaling laws based on the skewness of the content popularity. Our results suggest that for wireless networks with caching, when contents have skewed popularity, increasing the number of nodes monotonically increases the per node capacity.

Detecting daily activities is helpful for health care and clinical medicine. In this paper, we present ActDetector, a smartwatch based application which detects 8 common daily activities, including sitting, walking, running, going upstairs, going downstairs, eating, driving and sitting in a vehicle. By leveraging the built-in sensors on smartwatch, a multi-level classification system is proposed which considers both detection accuracy and energy efficiency. ActDetector is designed to work unobtrusively, no matter on which wrist the smartwatch is worn. We have implemented ActDetector on Sony Smartwatch 3 and evaluated its performance in real experiments involving 12 users. Experimental results show that ActDetector is energy efficient and can detect the daily activities with high accuracy.

It is critical to maintain the communication links between important social pairs. However, maintaining the social links between faraway nodes in wireless networks is extremely difficult. Although multi-hop transmission can be used, if two nodes in the routing path are out of the wireless transmission range, a network partition is possible. To address this problem, we adopt a cooperative amplify-and-forward strategy, where nodes (relays) cooperate to improve the signal strength at the destination. We formulate and study two optimization problems for maintaining the required link throughput: Min-Energy and Min-Relay, where the goal of Min-Energy is to minimize the power consumption of the relays, and the goal of Min-Relay is to minimize the number of active relays. Since the Min-Energy problem is a non-convex problem, we solve it based on an approximation technique and prove that our solution is a feasible, in fact optimal solution. We formulate the Min-Relay problem as an integer programming problem, and propose a polynomial-time algorithm which can select the minimum number of relays to maintain the social link. Evaluation results show that Min-Relay can significantly reduce the number of active relays compared to Min-Energy, while achieving comparable power consumption.

Caching in wireless ad hoc networks can reduce network traffic and content access delay, as nodes can retrieve contents from near neighbors rather than the faraway server. However, the fundamental performance limits of caching in wireless ad hoc networks have rarely been studied in an analytical manner. In this paper, we study the fundamental property of wireless networks with caching, i.e., the scaling laws of the network capacity based on cache size of individual node, the total size of unique content and the number of nodes in the network. We present an upper bound on network capacity, and present an achievable capacity lower bound, where we propose a caching scheme to show what capacity can actually be achievable. Our results suggest that the capacity of wireless ad hoc networks with caching can remain constant even as the number of nodes in the network increases. We also present numerical results and demonstrate that our results are consistent with existing analytical results under extreme conditions where the node communication scenario matches theirs.