Aspects of the present invention relate to a compact optics module for a head mounted display including a DLP image source comprising a polarized light source providing illumination light, a flat reflective polarizer to reflect the illumination light toward the DLP image source and transmit image light, wherein the illumination light is provided at an angle to the DLP, a quarter wave retarder to change the polarization state of the illumination light as it is reflected by the DLP image source thereby providing image light, at least one light trap to absorb a portion of off-state image light, a lens element to provide a viewable field of view of on-state image light in an image displayed by the DLP image source, and a combiner to reflect the on-state image light toward a user's eye and provide a see-through view of a surrounding environment.

See-through computer display systems

A wireless sensor network with multimedia capabilities typically consists of data sensor nodes, which sense, for instance, sound or motion, and video sensor nodes, which capture video of events of interest. In this survey, we focus on the video encoding at the video sensors and the real-time transport of the encoded video to a base station. Real-time video streams have stringent requirements for end-to-end delay and loss during network transport. In this survey, we categorize the requirements of multimedia traffic at each layer of the network protocol stack and further classify the mechanisms that have been proposed for multimedia streaming in wireless sensor networks at each layer of the stack. Specifically, we consider the mechanisms operating at the application, transport, network, and MAC layers. We also review existing cross-layer approaches and propose a few possible cross-layer solutions to optimize the performance of a given wireless sensor network for multimedia streaming applications.

/pdf/a-survey-of-multimedia-streaming-in-wireless-sensor-networks-50x2szpgry.pdf

A survey of multimedia streaming in wireless sensor networks

https://ir.nctu.edu.tw:443/bitstream/11536/15393/1/000299669400001.pdf

Review: From wireless sensor networks towards cyber physical systems

Embodiments describe registration in a wireless communication system. A method includes wirelessly transmitting over a WWAN a first registration message from a mobile device, wirelessly transmitting through the WWAN a second registration message to a WLAN access point and receiving at the mobile device access through the WLAN access point. According to another embodiment is a method for constructing a self-configuring ad-hoc network. The method can include receiving a GPS coordinate from a WWAN channel node at a management system and creating an initial topography based at least in part on the GPS coordinate to achieve a network connectivity with diverse routes between a plurality of nodes.

Methods and devices for interworking of wireless wide area networks and wireless local area networks or wireless personal area networks

Surveillance is one of the promising applications to which smart camera motes forming a vision-enabled network can add increasing levels of intelligence. We see a high degree of in-node processing in combination with distributed reasoning algorithms as the key enablers for such intelligent surveillance systems. To put these systems into practice still requires a considerable amount of research ranging from mote architectures, pixel-processing algorithms, up to distributed reasoning engines. This paper introduces MeshEye, an energy-efficient smart camera mote architecture that has been designed with intelligent surveillance as the target application in mind. Special attention is given to MeshEye's unique vision system: a low-resolution stereo vision system continuously determines position, range, and size of moving objects entering its field of view. This information triggers a color camera module to acquire a high-resolution image sub-array containing the object, which can be efficiently processed in subsequent stages. It offers reduced complexity, response time, and power consumption over conventional solutions. Basic vision algorithms for object detection, acquisition, and tracking are described and illustrated on real- world data. The paper also presents a basic power model that estimates lifetime of our smart camera mote in battery-powered operation for intelligent surveillance event processing.

http://mesheye.com/research/publications/conference_paper_9.pdf

MeshEye: a hybrid-resolution smart camera mote for applications in distributed intelligent surveillance

A system delivers multimedia services to mobile devices via a network by first registering a mobile device with a service manager connected to the mobile device via the network. Application service providers are connected to the service manager via the network. Each application service provider is associated with particular multimedia services. An environment description to be associated with the mobile device is acquired, and a particular application service provider is identified according to the environment description, and then selected services provided by the particular application service provider are delivered to the mobile device according to the environment description via the network.

Environment aware services for mobile devices

An image sensor network platform is developed for testing transmission of images over ZigBee networks that support multi-hopping. The ZigBee is a low rate and low power networking technology for short range communications, and it currently uses IEEE 802.15.4 MAC and PHY layers. Both ZigBee networking (NWK) and IEEE 802.15.4 MAC layer protocols are implemented on a single M16C microprocessor. Transport layer functionalities such as fragmentation and reassembly are performed at the application layer, since the ZigBee NWK does not have a fragmentation support. The multiple access scheme is CSMA/CA, therefore only the best effort multi-hop transmission of JPEG and JPEG-2000 images are tested; observations and resulting statistics are presented, and open issues are discussed.

Image transmission over IEEE 802.15.4 and ZigBee networks

The RPL routing protocol published in RFC 6550 was designed for efficient and reliable data collection in low-power and lossy networks. Specifically, it constructs a Destination Oriented Directed Acyclic Graph (DODAG) for data forwarding. However, due to the uneven deployment of sensor nodes in large areas, and the heterogeneous traffic patterns in the network, some sensor nodes may have much heavier workload in terms of packets forwarded than others. Such unbalanced workload distribution will result in these sensor nodes quickly exhausting their energy, and therefore shorten the overall network lifetime. In this paper, we propose a load balanced routing protocol based on the RPL protocol, named LB-RPL, to achieve balanced workload distribution in the network. Targeted at the low-power and lossy network environments, LB-RPL detects workload imbalance in a distributed and non-intrusive fashion. In addition, it optimizes the data forwarding path by jointly considering both workload distribution and link-layer communication qualities. We demonstrate the performance superiority of our LB-RPL protocol over original RPL through extensive simulations.

Load balanced routing for low power and lossy networks

With the increasing realization of the Internet-of-Things (IoT) and rapid proliferation of wireless sensor networks (WSN), estimating the location of wireless sensor nodes is emerging as an important issue. Traditional ranging based localization algorithms use triangulation for estimating the physical location of only those wireless nodes that are within one-hop distance from the anchor nodes. Multi-hop localization algorithms, on the other hand, aim at localizing the wireless nodes that can physically be residing at multiple hops away from anchor nodes. These latter algorithms have attracted a growing interest from research community due to the smaller number of required anchor nodes. One such algorithm, known as DV-Hop (Distance Vector Hop), has gained popularity due to its simplicity and lower cost. However, DV-Hop suffers from reduced accuracy due to the fact that it exploits only the network topology (i.e., number of hops to anchors) rather than the distances between pairs of nodes. In this paper, we propose an enhanced DV-Hop localization algorithm that also uses the RSSI values associated with links between one-hop neighbors. Moreover, we exploit already localized nodes by promoting them to become additional anchor nodes. Our simulations have shown that the proposed algorithm significantly outperforms the original DV-Hop localization algorithm and two of its recently published variants, namely RSSI Auxiliary Ranging and the Selective 3-Anchor DV-hop algorithm. More precisely, in some scenarios, the proposed algorithm improves the localization accuracy by almost 95%, 90% and 70% as compared to the basic DV-Hop, Selective 3-Anchor, and RSSI DV-Hop algorithms, respectively.

/pdf/a-hybrid-dv-hop-algorithm-using-rssi-for-localization-in-17s64fu7rc.pdf

A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks.

The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually positioned. Traditional methods iteratively localize these nodes by using triangulation. However, the inherent instability in wireless signals introduces an error, however minute it might be, in the estimated position of the target node. This results in the embedded error propagating and magnifying rapidly. Machine learning based localizing algorithms for large wireless sensor networks do not function in an iterative manner. In this paper, we investigate the suitability of some of these algorithms while exploring different trade-offs. Specifically, we first formulate a novel way of defining multiple feature vectors for mapping the localizing problem onto different machine learning models. As opposed to treating the localization as a classification problem, as done in the most of the reported work, we treat it as a regression problem. We have studied the impact of varying network parameters, such as network size, anchor population, transmitted signal power, and wireless channel quality, on the localizing accuracy of these models. We have also studied the impact of deploying the anchor nodes in a grid rather than placing these nodes randomly in the deployment area. Our results have revealed interesting insights while using the multivariate regression model and support vector machine (SVM) regression model with radial basis function (RBF) kernel.

Ghulam M. Bhatti

Papers

Environment aware services for mobile devices

Image transmission over IEEE 802.15.4 and ZigBee networks

Load balanced routing for low power and lossy networks

A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks.

Machine Learning Based Localization in Large-Scale Wireless Sensor Networks.