Zhen Ren

SiFi: exploiting VoIP silence for WiFi energy savings insmart phones

Since one-third of a smart phones battery energy is consumed by its WiFi interface, it is critical to switch the WiFi radio from its active or Constantly Awake Mode (CAM), which draws high power (726mW with screen off), to its sleep or Power Save Mode (PSM), which consumes little power (36mW). Applications like VoIP do not perform well under PSM mode however, due to their real-time nature, so the energy footprint is quite high. The challenge is to save energy while not affecting performance. In this paper we present SiFi: Silence prediction based WiFi energy adaptation. SiFi examines audio streams from phone calls and tracks when silence periods start and stop. This data is stored in a prediction model. Using this historical data, we predict the length of future silence periods and place the WiFi radio to sleep during these periods. We implement the design on an Android Smart phone and acheive 40% energy savings while maintaining high voice fidelity.

AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks

In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.

BodyT2: Throughput and time delay performance assurance for heterogeneous BSNs

Body sensor networks (BSNs) have been developed for a set of performance-critical applications, including smart healthcare, assisted living, emergency response, athletic performance evaluation, and interactive controls. Many of these applications require stringent performance assurance in terms of communication throughput and bounded time delay. While solutions exist in literature for providing joint throughput and time delay assurance by proposing specific MAC protocols or extensions, we provide this joint assurance in a novel radio-agnostic manner. In our approach, the underlying MAC and PHY layers can be heterogeneous and their details do not need to be known to upper layers like the resource management. Such a radio-agnostic performance assurance is critical because a range of radio platforms are adopted for practical body sensor usage. Our approach is based on a group-polling scheme that is essential for radio-agnostic BSN design. Through theoretical analysis, we prove that with the group-polling scheme, achieving joint throughput and time delay assurance is an NP-hard problem. For practical system deployment, we propose the BodyT2 framework that assures throughput and time delay performance in a heterogeneous BSN. Through both TelosB mote lab tests and real body experiments in an Android phone-centric BSN, we demonstrate that BodyT2 achieves superior performance over existing solutions.

SAS: Self-Adaptive Spectrum Management for Wireless Sensor Networks

Smart wireless sensor devices are becoming increas- ingly ubiquitous and are expected to be embedded in everyday objects in the near future. When these devices are deployed in overlapping or adjacent geographic areas, the unlicensed 2.4GHz ISM band will be crowded. To deal with the crowded spectrum, we propose SAS, a Self-Adaptive Spectrum management middle- ware for wireless sensor networks. SAS enables single-frequency MAC protocols with multi-frequency capability, so that an existing MAC protocol, like B-MAC, can automatically adapt to the least congested physical channel at runtime. We implemented SAS in TinyOS 2.1 with nesC and evaluated its performance with TelosB motes. Our performance results demonstrate that SAS improves the performance of existing single-frequency MAC protocols, like B-MAC. The use of SAS results in higher packet reception ratio and system throughput, and a lower packet delay and energy consumption.

A Self-Adaptive Spectrum Management Middleware for Wireless Sensor Networks

The vision of the Internet of Things, wherein everyday objects are embedded with smart wireless sensor devices, is making these sensor devices increasingly pervasive. As the density of their deployment in overlapping or adjacent areas increases, the contention for the unlicensed 2.4GHz ISM band will also increase. To deal with the crowded spectrum, nodes must use the channels more judiciously and be able to adapt by detecting and switching to the most available channel. The SAS middleware that we have developed, is a self-adaptive spectrum management middleware for wireless sensor networks that enhances single-frequency MAC protocols with multi-frequency capability, without any change in hardware. It allows a single-frequency MAC protocol, like B-MAC, to automatically adapt to the least congested physical channel at runtime. SAS supports a combination of receiver-initiated and sender-initiated schemes to decide when to switch the channel and which channel to switch to. We have implemented the B-MAC protocol integrated with SAS in TinyOS 2.1 on TelosB sensor devices and evaluated its performance on the conditions of varied data flows and the interference produced by a jammer. The results demonstrate that the integrated B-MAC protocol outperforms B-MAC in terms of packet reception ratio, system throughput, average packet delay, and energy consumption.