1 Introduction “Information at your fingertips anywhere, anytime” has been the driving vision of mobile computing for the past two decades. Throughrelentless pursuit of this vision, spurring innovations in wireless technology, energy-efficient portable hardwareand adaptive software, we have now largely attained this goal. Ubiquitous email and Web access is a reality that is experienced by millions of users worldwide through their BlackBerries, iPhones, Windows Mobile, and other portable devices. Continuing on this road, mobile Web-based services and location-aware advertising opportunities have begun to appear, triggering large commercial investments. Mobile computing has arrived as a lucrative business proposition. What will inspire our research in mobile computing over the next decade and beyond? We begin by considering two hypothetical mobile computing scenarios from the future. We then extract the deep assumptions implicit in thesescenarios, and use them to speculate on the future trajectory of mobile computing. We conclude that there are really two fundamentally distinct strategies atplay, and that the dialectic between these strategies will largely shape the mobile computing landscape of the future.
“信息随时随地都在您的指尖”
一直是移动计算的驱动愿景
过去二十年。通过不懈的追求
这一愿景，激发了无线技术，节能便携式硬件和自适应技术的创新
软件，我们现在基本上达到了这个目标。
无处不在的电子邮件和Web访问是现实
通过全球数百万用户体验
他们的黑莓，iPhone，Windows Mobile和
其他便携式设备。继续走这条道路，移动的基于Web的服务和位置感知广告机会已经开始出现，触发
大型商业投资。移动计算
已经成为一个有利可图的商业主张。
在未来十年甚至更长时间内，我们的移动计算研究能够激发什么？我们从一开始
从未来考虑两个假设的移动计算方案。然后，我们提取这些场景中隐含的深层假设，并使用它们
推测移动计算的未来发展轨迹。我们的结论是，实际上有两种根本不同的策略在起作用，而这些策略之间的辩证法将在很大程度上塑造这种策略
移动计算未来的景观。
2 Scenario 1: Lost Child Five-year old John is having a wonderful time with his family at the Macy’s Thanksgiving Day parade in Manhattan. Mid-way through the parade, John sees a group of friends in the crowd nearby. He shows his parents where his friends are, and tells them he is going over to meet them. Since his parents see responsible adults in the group, they are fine with John walking over to see his friends. An hour later, John’s parents walk over to where they expect to find him. To their shock, they discover that the friends have not seen John at all. He has been missing for an entire hour now, and John’s parents are very concerned. Searching for a lost child in a Manhanttan crowd is a daunting task. Fortunately, a police officer nearby is able to send out an amber alert via text message to all smartphone users within two miles. He requests them to upload all photographs they may have taken in the past hour to a secure web site that only the police can view. In a matter of minutes, the web site is populated with many photographs. New photographs continue to arrive as more people respond to the amber alert. With John’s parents helping him, the police officer searches these photographs with an application on his smartphone. His search is for the red plaid shirt that John was wearing. After a few pictures of Scottish kilts in the parade, a picture appears that thrills John’s parents. In a corner of that picture, barely visible, is a small boy in a red shirt sitting on the steps of a building. The police officer recognizes the building as being just two blocks further down the parade route, and contacts one of his fellow officers who is closer to that location. Within moments, the officer is with the boy. John is safe now, but he has a lot of explaining to do ...
2情景1：失去的孩子
五岁的约翰正在度过美好的时光
他的家人在梅西百货的感恩节游行
曼哈顿。约翰看到，在游行的中途
一群朋友在附近的人群中。他显示
他的父母在他的朋友所在的地方，告诉他们
正在过去见他们。由于他的父母在小组中看到负责任的成年人，他们对John很好
走过去看他的朋友们。一个小时后，约翰的
父母走到他们希望找到他的地方。
令他们震惊的是，他们发现朋友们没有
看见约翰。他一整个都失踪了
现在一小时，约翰的父母非常担心。
在Manhanttan人群中寻找失踪的孩子是
一项艰巨的任务。
幸运的是，附近的一名警察能够派遣
通过短信向两英里内的所有智能手机用户发出琥珀色警报。他要求他们
上传他们拍摄的所有照片
过去一小时到一个只有警察可以的安全网站
视图。在几分钟内，网站就会被填充
有很多照片。新照片继续
随着更多人回应琥珀警报而到达。
约翰的父母帮助他，警察
通过申请搜索这些照片
他的智能手机他搜索的是红色格子衬衫
约翰穿的。在游行队伍中苏格兰短裙的几张照片后，一张照片显得格外刺激
约翰的父母。在那张照片的一角，几乎没有
可见，是一个穿着红色衬衫的小男孩坐在上面
建筑的步骤。警察认出了这一点
建筑只是两个街区
游行路线，并与他的一名同事联系
谁离那个地方更近。片刻之内
军官和男孩在一起。约翰现在很安全，但他有一个
很多解释要做...
3 Scenario 2: Disaster Relief The Big One, measuring 9.1 on the Richter scale, has just hit Northern California. The entire Bay Area is one seething mass of humanity in anguish. Many highways, power cables and communication lines are severely damaged. Disaster on such a scale has not been seen since World War II. With limited manpower, unreliable communication and marginal transportation, disaster relief personnel are stretched to the limit. Internet infrastructure, including many key data centers, have been destroyed. The Googleplex has been reduced to a smoking hulk. In spite of heroic efforts, disaster relief is painfully slow and hopelessly inadequate relative to the scale of destruction. Sudden obsolescence of information regarding terrain and buildings is a major contributor to slow response. Vital sources of knowledge such as maps, surveys, photographs, building floor plans, and so on are no longer valid. Major highways on a map are no longer usable. Bridges, buildings, and landmarks have collapsed. GoogleEarth and GoogleMaps are now useless for this reqion. Even the physical topography of an affected area may be severely changed. Conducting search and rescue missions in the face of obsolete information is difficult and dangerous. New knowledge of terrain and buildings has to be reconstructed from scratch at sufficient resolution to make important life and death decisions in search and rescue missions. In desperation, the rescue effort turns to an emerging technology: camera-based GigaPan sensing. Using off-the-shelf consumer-grade cameras in smartphones, local citizens take hundreds of close-up images of disaster scenes. Transmission of these images sometimes occurs via spotty low-grade wireless communication; more often, the images are physically transported by citizens or rescue workers. The captured images are then stitched together into a zoomable panorama using compute-intensive vision algorithms. To speed up the process, small GigaPan robots that can systematically photograph a scene with hundreds of close-up images are air-dropped over the area for use by citizens. Slowly and painstakingly, detailed maps and topographical overlays are constructed bottom-up. As they become available, rescue efforts for those areas are sped up and become more effective. Rescuing trapped people is still dangerous, but at least the search teams are now armed with accurate information that gives them a fighting chance ...
3情景2：赈灾
The Big One，里氏9.1级，
刚刚袭击北加州。整个海湾
面积是痛苦的人类中的一个沸腾。
许多高速公路，电力电缆和通信
线条严重受损。这样的灾难
自第二次世界大战以来，没有看到过规模。同
有限的人力，不可靠的沟通和
边际交通，救灾人员
被拉伸到极限。互联网基础设施，包括许多关键数据中心，已被销毁。
Googleplex已经变成了一个吸烟的废船。
尽管做出了英勇的努力，但救灾却是痛苦的
相对于规模来说，缓慢且绝望地不足
破坏。
关于地形和建筑物的信息突然过时是导致响应缓慢的主要原因。重要的知识来源，如地图，
调查，照片，建筑平面图等
不再有效。地图上的主要高速公路是
不再可用。桥梁，建筑物和地标
已经崩溃了。 GoogleEarth和GoogleMaps是
现在对这个要求毫无用处。甚至受影响区域的物理地形也可能会发生严重变化。
面对的执行搜救任务
过时的信息既困难又危险。新
必须以足够的分辨率从头开始重建地形和建筑物的知识
搜索和救援任务中重要的生死决定。
无奈之下，救援工作转向了一项新兴技术：基于摄像头的GigaPan传感。当地公民在智能手机中使用现成的消费级相机，拍摄数百张灾难场景特写图像。这些图像的传输有时通过参差不齐的低级无线发生
通讯;更常见的是，图像是由公民或救援人员物理运输的。该
然后将拍摄的图像拼接成一个
使用计算密集型视觉的可缩放全景
算法。为了加快这个过程，小GigaPan
可以系统地拍摄场景的机器人
数以百计的特写图像都是空投的
在该地区供公民使用。
慢慢地，艰苦地，自下而上地构建详细的地图和地形覆盖。如
它们变得可用，这些区域的救援工作加快并变得更加有效。拯救被困人员仍然是危险的，但至少是
搜索团队现在拥有准确的信息，给他们一个战斗机会......
4 Reflecting on these Scenarios These scenarios embody a number of themes that will be central to the evolution of mobile computing over the next decade. We explore these themes next. Common to both scenarios is the prominent role of mobile devices as rich sensors. While their computing andcommunicaton roles continue to be important, it is their rich sensing role (image capture) that stands out most prominently in these scenarios. We use the term “rich” to connote the depth and complexityabout the real world that is being captured. This is in contrast to simple scalar data such as temperature, time and location that are involved in typical sensor network applications. When cell phones with integrated cameras first appeared, people wondered if they represented a solution in search of a problem. Would mobile users take so many photographs that this capability was worth supporting? Today, the value of this functionality is no longer questioned. Tomorrow the roles will be reversed: people will wonder why any digital camera lacks the wireless capability to transmit its images. Video capture, leading to even richer sensing and recording of the real world is also likely to gain traction. A second emergent theme is that of near-real-time data consistency. This is most apparent in the lost child scenario, where the only useful images are very recent ones. Pictures taken before the child was lost are useless in this context. Recency of data is also important in the disaster relief scenario. A major earthquake is often followed by aftershocks for hours or possibly days. These aftershocks can add to the damage caused by the original quake, and in some cases be the “tipping point” that triggers major structural and topographical changes. Regions that have already been mapped after the original quake may need to be remapped. The need for near-real-time data consistency forces rethinking of a long line of work in mobile computing that relates to the use of prefetching and hoarding for failure resiliency. The core concepts behind those techniques may still be valuable, but major changes in their implementations may have to be developed in order to apply them to the new context. In the disaster relief scenario, for example, many old maps and photographs may still be valid if the buildings and terrain involved have only been minimally affected. However, discovering whether it is acceptable to use hoarded information about them is a challenge. No central authority (e.g. a server) can answer this question with confidence. Only an on-the-spot entity (e.g. a user with a mobile device) can assess whether current reality is close enough to old data for safe reuse. That determination may involve human judgement, possibly assisted by software (e.g. a program that compares two images to estimate disruption). A third emergent theme is that of opportunism. This is most evident in the lost child scenario. The users who contribute pictures were completely unaware of their potential use in searching for the lost child. They took the pictures for some other reason, such as a funny float in the parade. But because of the richness of the sensed data, there are potentially “uninteresting” aspects of the image (e.g. small child in the corner of the picture) that prove to be very important in hindsight — it iscontextthat determines importance. Although the theme of opportunism also applies to simpler sensed data (e.g., anti-lock braking devices on cars transmit their GPS coordinates on each activation, enabling a dynamic picture of slick spots on roads to be obtained by maintenance crews), the richness of captured data greatly increases the chances for opportunistic reuse. An airport video image that was deemed uninteresting on 9/10/2001 may prove to be of high interest two days later because it includes the face of a 9/11 hijacker. With such opportunism comes, of course, many deep and difficult questions pertaining to privacy. While these questions already exist today with mining data from surveillance cameras, they will grow in frequency and significance as mobile users increasingly contribute their rich sensed data. One can easily imagine a business model that provides small rewards for contributors of such data, while reaping large profits by mining aggregated data for customers. A final emergent theme is the need to broaden our definition of “mobile computing” to embrace developments that lie well outside our narrow historical concerns. Examples include non-indexed image search in the lost child scenario and GigaPan technology in the disaster relief scenario. These may feel like science fiction, but they arereality today.
4反思这些情景
这些场景体现了许多主题
将成为移动计算发展的核心
在接下来的十年里。我们接下来探讨这些主题。
这两种情景的共同点是
移动设备作为丰富的传感器虽然他们的计算和沟通角色仍然很重要，但是他们的丰富感知角色（图像捕捉）才是
在这些场景中最突出的是突出的。我们
使用“富”这个词来表示正在捕捉的现实世界的深度和复杂性。
这与典型传感器网络应用中涉及的简单标量数据（例如温度，时间和位置）形成对比。当手机
随着集成摄像头的首次出现，人们想知道他们是否代表了寻找解决方案的解决方案
问题。移动用户会拍这么多照片吗？这个功能值得支持吗？
今天，这个功能的价值不再存在
质疑。明天角色将被逆转：
人们会想知道为什么任何数码相机都没有
无线传输其图像的能力。视频捕捉，导致更丰富的感应和录音
现实世界也可能获得牵引力。
第二个新兴主题是近乎实时的主题
数据一致性。这在失落中最为明显
子场景，其中唯一有用的图像非常有用
最近的。孩子失踪前拍的照片
在这种情况下是无用的。数据的新近度也是
在救灾方案中很重要。一个主要的
地震往往伴随着几个小时的余震
或者可能是几天。这些余震可以增加
原始地震造成的破坏，有些
案例是引发重大结构和地形变化的“临界点”。有的区域
已经在原始地震发生后进行了映射
需要重新映射。需要近乎实时的
数据一致性迫使人们重新思考
从事与移动计算相关的工作
预取和囤积故障恢复能力。该
这些技术背后的核心概念可能仍然存在
有价值的，但实施的重大变化
可能必须开发才能应用它们
新的背景。在救灾情景中，为
例如，许多旧地图和照片可能仍然存在
如果涉及的建筑物和地形有效，则有效
只受到了极少的影响。但是，发现使用有关它们的囤积信息是否可以接受是一项挑战。没有中央权威
（例如服务器）可以放心地回答这个问题。只有现场实体（例如，有用户的用户）
移动设备）可以评估当前的现实情况
与旧数据足够接近以便安全重用。这种决定可能涉及人类的判断
由软件辅助（例如，比较的程序）
估计中断的两个图像）。
第三个新兴主题是机会主义。
这在丢失的儿童情景中最为明显。该
贡献图片的用户完全没有意识到他们在搜索丢失时的潜在用途
儿童。他们出于其他原因拍了照片，
比如在游行中有趣的浮动。但是因为
感知数据的丰富性，有潜力
图像的“无趣”方面（例如小孩子）
在事后证明是非常重要的 - 在图片的角落 - 它是确定的背景
重要性。虽然机会主义的主题也是如此
适用于更简单的感测数据（例如，汽车上的防抱死制动装置传输其GPS坐标
每次激活，都能实现光滑的动态画面
维修人员获得的道路上的斑点），
捕获数据的丰富性大大增加了
机会性重用的机会。机场视频
在9/10/2001被认为无趣的形象
两天后可能被证明具有高度兴趣，因为它包括了9/11劫机者的面孔。同
当然，这种机会主义很多都是深刻的
与隐私有关的疑难问题。虽然这些
今天已经存在的问题与挖掘数据有关
监控摄像机，它们会频繁增长
移动用户越来越多地贡献他们丰富的感知数据。人们很容易想象出一种能够提供小额奖励的商业模式
这些数据的贡献者，同时获得巨额利润
通过为客户挖掘汇总数据。
最后一个突出的主题是需要扩大我们对“移动计算”的定义，以接受远远超出我们狭隘的历史关注的发展。示例包括非索引图像
在救灾情景中搜索丢失的儿童情景和GigaPan技术。这些可能会感觉到
像科幻小说，但它们今天是现实。