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【智慧城市与ORBIT平台】智慧城市是现代城市发展的新趋势，旨在通过信息技术的深度融合，提高城市管理效率，提升居民生活质量。在这一背景下，智能手机作为数据密集型嵌入式传感应用的重要工具，发挥着越来越重要的作用。"ORBIT：智能手机为基础的数据密集型嵌入式感知平台"是一个创新的解决方案，它旨在克服现有智能手机在执行此类任务时所面临的挑战。 ORBIT平台由三层架构构成，分别是智能手机、节能外围板和云端服务。这种分层设计使得智能手机能够连接到能效高的外设板，或利用云端资源，以弥补其在实时处理、能耗以及用户友好型嵌入式编程支持方面的不足。通过这种架构，ORBIT能够智能地将任务分配到不同层次，以最小化系统的功耗，实现资源的优化利用。 ORBIT的核心特性之一是基于配置的任务分割。这允许系统根据任务特性和资源需求，动态调整处理任务的分配，从而实现最佳的性能和能效平衡。此外，平台提供了一个数据处理库，内含自适应延迟/质量权衡机制和多线程数据分区策略，进一步优化了资源使用。为了简化开发者的工作并增强编程灵活性，ORBIT引入了基于注释的编程API。这种API使得应用程序开发更为简便，同时允许开发者根据需要调整和定制功能。通过广泛的微基准评估和两个实际案例——地震传感和多摄像头3D重建——ORBIT的通用设计得到了验证，证明其在各种数据密集型应用中的适用性和有效性。总结起来，ORBIT平台是智慧城市框架下，解决智能手机在数据密集型嵌入式传感应用中问题的有效工具。它通过创新的架构设计、任务调度策略和友好的开发环境，推动了智能手机在智慧城市领域的应用，为未来智慧城市的建设提供了新的思路和技术支撑。

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ORBIT: A Smartphone-Based Platform for Data-Intensive

Embedded Sensing Applications

Mohammad-Mahdi Moazzami

∗

Dennis E. Phillips

∗

Rui Tan

†

Guoliang Xing

∗

Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

Advanced Digital Sciences Center, Illinois at Singapore

ABSTRACT

Owing to the rich processing, multi-modal sensing, and versatile

networking capabilities, smartphones are increasingly used to build

data-intensive embedded sensing applications. Howev er, various

challenges must be systematically addressed before smartphones

can be used as a generic embedded sensing platform, including

high power consumption, lack of real-time functionality and user-

friendly embedded programming support. This paper presents OR-

BIT, a smartphone-based platform for data-intensive embedded se-

nsing applications. ORBIT features a tiered architecture, in which

a smartphone can interface to an energy-efﬁcient peripheral board

and/or a cloud service. ORBIT as a platform addresses the short-

comings of current smartphones while utilizing their strengths. OR-

BIT provides a proﬁle-based task partitioning allowing it to intel-

ligently dispatch the processing tasks among the tiers to minimize

the system power consumption. ORBIT also provides a data pro-

cessing library that includes two mechanisms namely adaptive de-

lay/quality trade-off and data partitioning via multi-threading to op-

timize resource usage. Moreover, ORBIT supplies an annotation

based programming API for developers that signiﬁcantly simpliﬁes

the application development and provides programming ﬂexibility.

Extensive microbenchmark evaluation and two case studies includ-

ing seismic sensing and multi-camera 3D reconstruction, validate

the generic design of ORBIT.

Categories and Subject Descriptors

C.3 [Special-Purpose and Application-Based Systems]: Real-

time and embedded systems, signal processing systems

Keywords

Smartphone, embedded sensing, data processing, data-intensive ap-

plications

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies

are not made or distributed for proﬁt or commercial advantage and that

copies bear this notice and the full citation on the ﬁrst page. Copyrights

for components of this work owned by others than ACM must be honored.

Abstracting with credit is permitted. To cop y otherwise, or republish, to

post on servers or to redistribute to lists, requires prior speciﬁc permission

and/or a fee. Request permissions from Permissions@acm.org.

IPSN’15, April 14-16, 2015, Seattle, WA, USA

http://dx.doi.org/10.1145/2737095.2737098.

1. INTRODUCTION

The ubiquity of smartphones and their multi-modal sensing ca-

pabilities have enabled a wide spectrum of mobile sensing appli-

cations. These applications are usually human-centric in that the

smartphone utilizes on-board sensors to sense people and character-

istics of their contexts. Different from these human-centric sensing

applications, this paper considers an emerging class of smartphone-

based data-intensive embedded sensing applications. In contrast to

the people-centric nature of participatory sensing, smartphones in

these applications are embedded into environments to sense and

interact with the physical world autonomously over long periods

of time. For instance, in the Floating Sensor Network project [9],

smartphone-equipped drifters are rapidly deployed to collect real-

time data about the ﬂow of water through a river. The smartphone’s

GPS allows the drifter to measure volume and direction of wa-

ter ﬂow based on its real-time l ocation and transmit the data back

to the server through cellular networks. Smartphones have also

been employed for monitoring earthquakes [7], volcanoes [13], and

even operating miniature satellites [22]. Another important class of

smartphone-based embedded systems is cloud robots [11] [4].By

integrating smartphones, these robots can lev erage a plethora of

phone sensors to realize complex sensing and navigation capabil-

ities and ofﬂoad compute-intensive cognitive tasks like image and

voice recognition to the cloud.

Compared with the traditional mote-class sensing platforms, sm-

artphones have several salient advantages that make them promis-

ing system platforms for the aforementioned embedded applica-

tions. These features include high-speed multi-core processors that

are capable of e xecuting advanced data processing algorithms, mul-

tiple network interfaces, various integrated sensors, friendly user

interfaces and advanced programming languages. Moreover, the

price of smartphones has been dropping signiﬁcantly in the last

decade. Many Android phones with reasonable conﬁgurations (up

to 800 MHz CPU and 2 GB memory) cost less than US$50 [18].

However, several challenges must be addressed before smart-

phones can be used as a system platform for embedded sensing

applications. First, the smartphones, which are designed to provide

several days of battery life, are ill-suited for many embedded sens-

ing applications that must operate unattended for long periods of

time. Many of today’s embedded applications are inherently data-

intensive in that sensors must sample at high rates (e.g., 100 Hz

in seismic sensing [29]). The continuous sensor sampling can pre-

vent the smartphone from entering sleep state, leading to battery

depletion in a few hours. Moreover, the current major smartphone

operating systems (OSes) do not provide real-time functionalities,

such as constant sampling rate, precise timestamping, and program-

ming interfaces for expressing timing requirements, which are cru-

cial to many embedded sensing applications. For instance, our

measurements show that the USB hardware interrupt of Android

phones suffers an unpredictable delay of up to 5 ms, which makes

it impossible to achieve a high constant sampling rate. Lastly, the

smartphone programming environment, although simplifying many

programming tasks in the life cycle of embedded systems such as

debugging, remote data logging, visualization, and software main-

tenance, lacks important embedded programming support such as

resource-efﬁcient signal processing libraries and communication/-

control primitives for peripheral sensors.

In this paper, we take the ﬁrst step toward addressing these chal-

lenges collectively. We present ORBIT, a smartpho

ne-based plat-

for

m for embedded sensing systems. In particular, ORBIT lever-

ages off-the-shelf smartphones to meet the energy-efﬁciency and

timeliness requirements of data-intensive embedded sensing appli-

cations. ORBIT is based on a tiered architecture that comprises up

to three tiers: the cloud, the smartphone, and one or more energy-

efﬁcient peripheral boards (referred to as extBoard) that are inter-

faced with the smartphone. A number of extBoard platforms are

currently available, such as Arduino [1] and IOIO [14]. There-

fore, if the built-in sensors on the smartphones are not suitable

for sensing applications, these boards can readily integrate vari-

ous accessories, such as external sensors, to an Android phone via

USB or bluetooth interface. We conduct a measurement study on

the latency and power consumption of Android smartphones and

extBoard platforms. Our results show that the two platforms have

highly heterogeneous but complementary power/latency proﬁles:

smartphone features higher energy efﬁciency due to its faster pro-

cessing capability while yielding poor timing accuracy due to the

overhead of OS. These results have important implication f or ef-

ﬁcient task partitioning. In particular, while the smartphone and

cloud should handle long-running compute-intensive tasks, time-

critical functions such as high-rate sensor sampling and precise

event timestamping must be shifted to the extBoard owing to its

hardware timers and efﬁcient interrupt handling.

Motivated by the above observations, we propose a task par-

titioning framework that assigns tasks to different tiers based on

their time-criticality, compute-intensity, and heterogenous laten-

cy/power consumption proﬁles. Furthermore, to take advantage

of the increasing availability of multiple cores on smartphones,

ORBIT implements a data partitioning scheme that decomposes

matrix-based computation into multiple threads. ORBIT also in-

tegrates a data processing library that supports high-level Java an-

notated application programming. The design of this library fa-

cilitates the resource management of the embedded applications by

promoting a delay/quality trade-off mechanism. To enable dynamic

task dispatch and runtime task proﬁling, we develop an ORBIT

runtime environment consisting of task controllers running on each

tier. These controllers coordinate task execution through a uniﬁed

messaging protocol. Owing to these features, ORBIT is a powerful

system toolkit to build a wide spectrum of data-intensive embedded

sensing applications.

This paper makes the following contributions. First, we con-

duct systematic measurement and modeling to understand the op-

portunities as well as the challenges for using smartphones for data-

intensive embedded sensing applications. Our measurement results

are also useful for the design of a broad class of smartphone-based

sensing systems. Second, we provide an implementation of several

data processing algorithms as a library as well as several mecha-

nisms that improve the efﬁciency of data processing algorithms for

both the smartphone and the extension board. Several components

of ORBIT bear some similarity with existing embedded system

platforms [5, 10, 23, 28]. However, to our best kno wledge, OR-

BIT is the ﬁrst general-purpose, e x tensible, application-aware, and

end-to-end sensing and processing platform for smartphones-based

data-intensive embedded applications

. Lastly, we demonstrate

the generality and ﬂexibility of ORBIT as a platform by presenting

our experience in prototyping two applications upon ORBIT: seis-

mic sensing and multi-camera 3D reconstruction. The ﬂ exible task

partitioning and dispatching framework allows ORBIT to adapt to

different task structures, application deadlines, and communication

delays. The experiments show ORBIT reduces energy consump-

tionbyupto50% compared to baseline approaches.

2. RELATED WORK

Mobile sensing based on smartphones has recently received sig-

niﬁcant interests. Most studies focus on the issues related to human-

centric context, including coordination among multiple concurrent

sensing applications [16, 17, 15] and sensing algorithms such as

context classiﬁers [3]. Recently, smartphones have been used in

a number of embedded sensing applications. In [7], smartphones

are used to build an earthquake early warning system using an on-

board accelerometer. In the Floating Sensor Network project [9],

smartphone-equipped drifters are deployed to monitor waterways

and collect real-time volume and direction of water ﬂow based on

the phone’ s GPS. The NASA PhoneSat project [22] has launched

low-cost satellites equipped with Android smartphones. Controlled

by a smartphone, such small satellites could perform various tasks

such as earth observation and space debris tracking. Several re-

cent efforts focus on building cloud robots [11] that integrate smart-

phones with robots. The phone’s built-in sensors are used for sens-

ing and navigation, while compute-intensive tasks like image and

voice recognition are ofﬂoaded to the cloud.

Various task ofﬂoading schemes for smartphones have been de-

veloped recently. Spectra [8] allows programmers to specify task

partitioning plans given application-speciﬁc service requirements.

Chroma [2] aims to reduce the burden on manually deﬁning the

detailed partitioning plans. Medusa [25] features a distributed run-

time system to coordinate the execution of tasks between smart-

phones and cloud. Turducken [28] adopts a hierarchical po wer

management architecture, in which a laptop can ofﬂoad lightweight

tasks to tethered PDAs and sensors. While Turducken provides a

tiered hardware architecture for partitioning, it relies on the appli-

cation developer to design a partitioned application across the tiers

to achieve energy efﬁciency.

Different from these task partitioning schemes, ORBIT dispatches

the execution of sensing and processing tasks in a smartphone-

based multi-tier architecture to achieve data-intensive applications

requirements. ORBIT maximizes the battery lifetime subject to

the application-speciﬁc latency constraints. Moreover, in order to

support ﬁne-grained task partitioning across the tiers, the devel-

oper speciﬁes the application’s task structure as well as real-time

requirements via either Java annotations or an XML-based applica-

tion model provided by ORBIT. ORBIT also provides a messaging

interface to support uniﬁed data passing mechanism between het-

erogenous tiers and between different application components.

The MAUI system [5] enables a ﬁne-grained ofﬂoading mecha-

nism to prolong the smartphone’s battery lifetime. Ho wev er, MAUI

relies on the properties of the Microsoft .NET managed code envi-

ronment to identify the functions that can be executed remotely.

When a function is executed remotely, MAUI assumes the energy

associated with its local execution is saved. I n contrast, ORBIT

does not rely on any language speciﬁc environment and its measure-

ment-based power proﬁles account for many realistic po wer char-

acteristics such as CPU sleep, wake up and tail time.

The source code of ORBIT is available at https://github.com/msu-sensing/ORBIT

The Wishbone system [23] also features a task dispatch scheme.

Unlike Turducken, Wishbone uses a proﬁle-based approach to ﬁnd

the optimal partition. It only considers two tiers: in-network and

on-server. Unlike MAUI, Wishbone relies on the timing proﬁle

only and does not account for the power consumption. ORBIT

differs from Wishbone in several ways. Wishbone uses the CPU

and network timing proﬁles only to ﬁ nd the optimal task partition,

while ORBIT considers the measured latency and power consump-

tion, which leads to more energy-efﬁcient task partitions. More-

ov er, W ishbone depends on the timing proﬁles based on sample

data under the assumption that the sample data can represent actual

runtime data. However, our measurement study shows that the sig-

nal processing timing proﬁles can exhibit signiﬁcantly variations

in real scenarios. To address this, ORBIT measures the statistical

timing proﬁles at runtime, and periodically reﬁnes the partitioning

results. Moreover, Wishbone formulates the partitioning problem

as a 0/1 integer linear programming problem and thus supports two

tiers only. In contrast, ORBIT formulates the problem as a non-

linear optimization problem and supports three or more tiers.

RTDroid [30] tackles the lack of hard real-time capability of An-

droid system and addresses the problem by redesigning and replac-

ing several Android components in Dalvik, e.g., Looper-Handler

and Alarm-Manager. In contrast, ORBIT requires no changes to the

Android system. ORBIT accounts for statistical properties of task

execution, and ﬁnds the best execution assignment by its task parti-

tioning mechanism. Hence, although RTDroid and ORBIT address

different sets of issues, they are complementary. In fact, ORBIT

can run on RTDroid and the ORBIT-based sensing applications can

beneﬁt from both.

Similar to ORBIT, EmStar [10] provides an environment to im-

plement distributed embedded systems for sensing applications based

on Linux-class Microservers. However ORBIT takes one major

step further and proposes a design based on smartphones for the

purpose they are not originally designed for, which is embedded

systems. This difference in underlying technology leads to totally

different design and implementation. Although EmStar and ORBIT

have similar modular designs, unlike ORBIT, EmStar does not have

any partitioning mechanism and it is not strictly tiered. More im-

portantly, ORBIT provides a library of data processing algorithms

that are ef ﬁcient on the resource-constrained smartphone and ex-

tension board. This is not a design goal of EmStar.

3. MOTIVATION AND SYSTEM OVERVIEW

In this section, we discuss the motivation of using smartphone

as a system platform for data-intensive embedded sensing applica-

tions and the design objectives of ORBIT.

3.1 Motivation and Challenges

Mote-class sensing platforms such as TelosB have been widely

adopted by embedded sensing applications in the past decade. How-

ever, due to the limited processing and storage capabilities, they

are ill-suited for high-sampling-rate sensing applications. Recently,

several single-board computers such as Gumstix [12], SheevaPlug

[20], and Raspberry Pi [26], which are equipped with rich process-

ing and storage capabilities, have been increasingly used in embed-

ded applications. However, their designs are not particularly opti-

mized for lo w-power sensing. Moreover, without on-board sensors

and wireless interfaces, they need to be equipped with various pe-

ripherals for different applications.

Different from the above platforms, commercial off-the-shelf sm-

artphones offer several salient advantages that make them a promis-

ing system platform for data-intensive embedded sensing applica-

tions. The advantages include rich computation and storage re-

(a) Seismic ORBIT node (b) Robotic ORBIT node

Figure 1: ORBIT nodes for seismic sensing and robots.

sources, multiple network interfaces and sensing modalities, in-

creasing available multi-core architecture and low cost. Moreover,

smartphones come with advanced programming languages and frie-

ndly user interfaces, such as touch screen to enable rich and inter-

active display, unlike the limited user interfaces of motes and em-

bedded computers (e.g., LED and buttons).

However, we still face the following major challenges in building

an embedded sensing platform based on COTS smartphones:

(1) High power consumption: The smartphone power manage-

ment schemes are designed to adapt to user activities to extend bat-

tery time. However, they are not suitable for untethered embedded

sensing systems. If the smartphone samples sensors continually, its

CPU cannot enter a deep sleep state to save energy. Low-power co-

processors (e.g., M7 in iPhone5s) can handle continuous sampling,

but are available on a few high-end models only.

(2) Lack of real-time functionalities: Many sensing applications

have stringent real-time requirements, such as constant sampling

rate and precise timestamping. However, modern smartphone OSes

are not designed for meeting these real-time requirements. For in-

stance, sensor sampling can be delayed by high-priority CPU tasks

such as Android system services or user interface drawing. Our

measurements show that the software timer provided by Android

may be blocked by Android core system services by up to 110 mil-

liseconds. Moreover, Android programming library does not pro-

vide the native interfaces that allow developers to express timing

requirements.

(3) Lack of embedded programming support: The program-

ming environment of smartphone is designed to facilitate the devel-

opment of networked, human-centric mobile applications. How-

ever, it lacks important embedded programming support such as

resource-efﬁcient signal processing libraries and uniﬁed primitives

for controlling and communicating w ith peripheral accessories such

as external sensors.

3.2 System Overview

In this paper, we present ORBIT, which is designed to address

the above three major challenges. An ORBIT node comprises an

Android smartphone, an extBoard (e.g., IOIO [14] and Arduino

[1]), and possibly a runtime system on the cloud. The extBoard

is connected to the smartphone through a USB cable or bluetooth

for communication. It is equipped with a low-power MCU, e.g.,

ATmega2560 with 16 MHz frequency, 8 KB RAM, and an analog-

to-digital (A/D) convertor that can integrate various analog sensors.

Fig. 1 shows two ORBIT prototypes, a seismic monitoring node

and a robot sensing node that are used in the ev aluation (cf. Section

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