Context-for-wireless

Abstract

Ubiquitous connectivity on mobile devices will enable numerous new applications in healthcare and multimedia. We set out to check how close we are towards ubiquitous connectivity in our daily life. The findings from our recent field-collected data from an urban university population show that while network availability is decent, the energy cost of network interfaces poses a great challenge. Based on our findings, we propose to leverage the complementary strength of Wi-Fi and cellular networks by choosing wireless interfaces for data transfers based on network condition estimation. We show that an ideal selection policy can more than double the battery lifetime of a commercial mobile phone, and the improvement varies with data transfer patterns and Wi-Fi availability.We formulate the selection of wireless interfaces as a statistical decision problem. The key to attaining the potential battery improvement is to accurately estimate Wi-Fi network conditions without powering up its network interface. We explore the use of different context information, including time, history, cellular network conditions, and device motion, for this purpose. We consequently devise algorithms that can effectively learn from context information and estimate the probability distribution of Wi-Fi network conditions. Simulations based on field-collected traces show that our algorithms can improve the average battery lifetime of a commercial mobile phone for a three-channel electrocardiogram (ECG) reporting application by 39%, very close to the theoretical upper bound of 42%. Finally, our field validation of our most simple algorithm demonstrates a 35% improvement in battery lifetime.

Energy Efficient Network Selection Using 802.16G Based GSM Technology

“…The network selection process is triggered by any of the following events: (i) new networks are discovered, (ii) current network conditions change, (iii) device receive handover commands from the network and (iv) policy changes are initiated by the user or application or operator. The focal point of our research is to obtain network maps from IIS service (Rahmati and Zhang, 2007), detect Network Link down by implementing Media Independent Event Service (MIES) functions and provide IIS information to the cost function to enable energy efficient handover.…”

Platys: From Position to Place‐Oriented Mobile Computing

“…Similarly, false negative ratio (FN(%)) is defined as the percentage of cases when the algorithm does not detect an event when it is available in the ground truth divided by the total number of cases. As shown in figure 6a, we find that to achieve very low FP(%) values, base station set (BSSET; see Rahmati and Zhong [2007]) and mean squared error (MSE; see Prasithsangaree, Krishnamurthy, and Chrysanthis 2002;Varshavsky et al 2007)-based algorithms need very We use a digital power monitoring device from Monsoon Solutions 4 to measure the energy consumptions for event sensing on Android smartphones (Google Nexus One). Extensive trials are done to avoid sensitive fluctuations in power consumption.…”

Human time-scale duty cycle for opportunistic WiFi based mobile networks

“…The energy consumption of WiFi have been studied in various works such as [18], [19] and [20]. However, some works have continued to study the application of this technology to opportunistic networks [21].…”