Point&Connect (P&C) offers an intuitive and resilient device pairing solution on standard mobile phones. Its operation follows the simple sequence of point-and-connect: when a user plans to pair her mobile phone with another device nearby, she makes a simple hand gesture that points her phone towards the intended target. The system will capture the user’s gesture, understand the target selection intention, and complete the device pairing. P&C is intentionbased, intuitive, and reduces user efforts in device pairing. The main technical challenge is to come up with a simple system technique to effectively capture and understand the intention of the user, and pick the right device among many others nearby. It should further work on any mobile phones or small devices without relying on infrastructure or special hardware. P&C meets this challenge with a novel collaborative scheme to measure maximum distance change based on acoustic signals. Using only a speaker and a microphone, P&C can be implemented solely in user-level software and work on COTS phones. P&C adds additional mechanisms to improve resiliency against imperfect user actions, acoustic disturbance, and even certain malicious attacks. We have implemented P&C in Windows Mobile phones and conducted extensive experimental evaluation, and showed that it is a cool and effective way to perform device pairing.

We present an algorithm enabling localization of moving wireless devices in an indoor setting. The method uses only RF signal strength and can be implemented without specialized hardware. The mobility of the users is modeled by learning a function mapping a short history of signal strength values to a 2D position. We use radial basis function (RBF) fitting to learn a reliable estimate of a mobile node’s position given its past signal strength measurements. Even though we deal with extremely noisy measurements in a cluttered indoor setting, nodes are not required to be stationary during measurement or learning. We evaluate our algorithm in a real indoor setting using MicaZ motes, achieving an average localization accuracy of 1.3 m. In our experiments, using historical data improves the localization accuracy by almost a factor of two compared to using only the most current measurements.

Many wireless sensor network applications require knowledge of node placement in order to make sense of sensor data in a spatial context. Networks of mobile sensors require position updates for navigation through the sensing region. The global positioning system is able to provide localization information, however in many situations it cannot be relied on, and alternative localization methods are required. We propose a technique for the localization and navigation of a mobile robot that uses the Doppler-shift in frequency observed by stationary sensor nodes. Our experimental results show that, by using observed RF Doppler shifts, a robot is able to navigate through a sensing region with an average localization error of 1.68 meters.

Abstract. Overthepastdecadewehavewitnessedtheevolutionof wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation. 1

The ability to move energy around makes it feasible to build distributed energy storage systems that can robustly extend the lifetime of networked sensor systems. eShare supports the concept of energy sharing among multiple embedded sensor devices by providing designs for energy routers (i.e., energy storage and routing devices) and related energy access and network protocols. In a nutshell, energy routers exchange energy sharing control information using their data network while sharing energy freely among connected embedded sensor devices using their energy network. To improve sharing efficiency subject to energy leakage, we develop an effective energy charging and discharging mechanism using an array of ultra-capacitors as the main component of an energy router. We extensively evaluate our system under six real-world settings. Results indicate our charging and discharging control can effectively minimize the energy leaked away. Moreover, the energy sharing protocol can quantitatively share 113J energy with 96.82 % accuracy in less than 2 seconds.

Recent technological advances have enabled the development of low-cost, low-power, and multifunctional sensor devices. These nodes are autonomous devices with integrated sensing, processing, and communication capabilities. In general, wireless sensor networks intend to provide information on spatio-temporal characteristics of the observed physical world. Hence, it is necessary to associate sensed data with locations, making data geographically meaningful. A number of applications, such as object tracking, environment monitoring, inherently rely on location information. Besides, location information also supports fundamental network layer services, such as topology control, routing, clustering, and so on. Hence, Localization, a mechanism for autonomously discovering and establishing spatial relationships among sensor nodes, is of great importance in the development of wireless sensor networks. This survey reviews diverse physical measuring abilities of sensor nodes, discusses issues in localization algorithm design, presents the state-of-the-art localization techniques, and finally suggests future directions in localization studies. Many localization approaches are proposed based on diverse positioning principles, environmental constrains, accuracy requirements, etc., making them suitable/unsuitable for different applications. This survey in depth elaborates and compares existing approaches from two aspects: physical measurement and network-wide localization. The design tradeoffs of localization algorithms, as well as their advantages and disadvantages, are emphasized for comparison. Among these localization techniques, no specific algorithm is a clear favorite across the spectrum. In conclusion, localization is a new and exciting field, with new algorithms, hardware, and applications being developed at a feverish pace. A lot of work still needs to be done to realize practical applications for wireless sensor networks.

We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person’s mobile phones. Since a person’s motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones ’ IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a twomeasurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed.

Having access to accurate position information is a key requirement for many wireless sensor network applications. We present the design, implementation and evaluation of SpiderBat, an ultrasound-based ranging platform designed to augment existing sensor nodes with distance and angle information. SpiderBat features independently controllable ultrasound transmitters and receivers, in all directions of the compass. Using a digital compass, nodes can learn about their orientation, and combine this information with distance and angle measurements using ultrasound. To the best of our knowledge, SpiderBat is the first ultrasound-based sensor node platform that can measure absolute angles between sensor nodes accurately. The availability of angle information enables us to estimate node positions with a precision in the order of a few centimeters. Moreover, our system allows to position nodes in multi-hop networks where pure distance-based algorithms must fail, in particular in sparse networks, with only a single anchor node. Furthermore, information on absolute node orientations makes it possible to detect whether two nodes are in line-of-sight. Consequently, we can detect the presence of obstacles and walls by looking at patterns in the received ultrasound signal.

Precise indoor localization of wireless nodes remains a challenge today. While there are radio-frequency (RF) methods that offer significant advantages, the balance between accuracy, range, and cost is suboptimal for many applications. Radio interferometry has been shown to be effective outdoors, however, its applicability indoors has not been demonstrated mainly due to its sensitivity to multipath. This paper presents a roadmap outlining how the method can be enhanced to advance the state-of-the-art in indoor RF localization. Categories and Subject Descriptors C.2.4 [Computer-Communications Networks]: Distributed

Abstract. Mobile sensors require periodic position measurements for navigation around the sensing region. Such information is often obtained using GPS or onboard sensors such as optical encoders. However, GPS is not reliable in all environments, and odometry accrues error over time. Although several localization techniques exist for wireless sensor networks, they are typically time consuming, resource intensive, and/or require expensive hardware, all of which are undesirable for lightweight mobile nodes. We propose a technique for obtaining angle-of-arrival information that uses the wheel encoder data from the mobile sensor, and the RF Doppler-shift observed by stationary nodes. These sensor data are used to determine the angular separation between stationary beacons, which can be used for navigation. Our experimental results demonstrate that using this technique, a robot is able to determine angular separation between four pairs of sensors in a 40 x 40 meter sensing region with an average error of 0.28 radian. 1