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Wireless Barcode Scanner

2009-09-08T05:09:00.000-07:00

Our wireless barcode scanners, also known as mobile computers and portable data terminals and data collectors, function as multi-use tools for your advanced data capture needs. We carry several different types of barcode scanner equipped portable data terminals and data collectors. Choices include mobile computers with batch or wireless (802.11 b/g) data capture, Windows Mobile, Linux-based, or Palm OS operating systems, 1D or 2D barcode scanning capabilities and more.

Wireless sensor network

2009-08-19T09:19:00.000-07:00

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations.^[1]^[2] The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control.^[1]^[3]

In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. The envisaged size of a single sensor node can vary from shoebox-sized nodes down to devices the size of grain of dust,^[1] although functioning 'motes' of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few pence , depending on the size of the sensor network and the complexity required of individual sensor nodes.^[1] Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.^[1]

A sensor network normally constitutes a wireless ad-hoc network, meaning that each sensor supports a multi-hop routing algorithm (several nodes may forward data packets to the base station).

In computer science and telecommunications, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year.

Applications

2009-08-19T09:18:00.000-07:00

The applications for WSNs are many and varied, but typically involve some kind of monitoring, tracking, and controlling. Specific applications for WSNs include habitat monitoring, object tracking, nuclear reactor control, fire detection, and traffic monitoring. In a typical application, a WSN is scattered in a region where it is meant to collect data through its sensor nodes.

Area monitoring

Area monitoring is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. For example, a large quantity of sensor nodes could be deployed over a battlefield to detect enemy intrusion instead of using landmines.^[4] When the sensors detect the event being monitored (heat, pressure, sound, light, electro-magnetic field, vibration, etc), the event needs to be reported to one of the base stations, which can take appropriate action (e.g., send a message on the internet or to a satellite). Depending on the exact application, different objective functions will require different data-propagation strategies, depending on things such as need for real-time response, redundancy of the data (which can be tackled via data aggregation and information fusion^[5] techniques), need for security, etc.

Environmental monitoring

A number of WSN deployments have been done in the past in the context of environmental monitoring^[6].

Many of these have been short lived, often due to the prototype nature of the projects. Examples of longer-lived deployments are monitoring the state of permafrost in the Swiss Alps: The PermaSense Project, PermaSense Live Data Browser and glacier monitoring.

Industrial Monitoring

Water/Wastewater Monitoring

There are many opportunities for using wireless sensor networks within the water/wastewater industries. Facilities not wired for power or data transmission can be monitored using industrial wireless I/O devices and sensors powered using solar panels or battery packs. As part of the American Recovery and Reinvestment Act (ARRA), funding is available for some water and wastewater projects in most states.

Landfill Ground Well Level Monitoring and Pump Counter

Wireless sensor networks can be used to measure and monitor the water levels within all ground wells in the landfill site and monitor leachate accumulation and removal. A wireless device and submersible pressure transmitter monitors the leachate level. The sensor information is wirelessly transmitted to a central data logging system to store the level data, perform calculations, or notify personnel when a service vehicle is needed at a specific well.

It is typical for leachate removal pumps to be installed with a totalizing counter mounted at the top of the well to monitor the pump cycles and to calculate the total volume of leachate removed from the well. For most current installations, this counter is read manually. Instead of manually collecting the pump count data, wireless devices can send data from the pumps back to a central control location to save time and eliminate errors. The control system uses this count information to determine when the pump is in operation, to calculate leachate extraction volume, and to schedule maintenance on the pump. ^[7]

Flare Stack Monitoring

Landfill managers need to accurately monitor methane gas production, removal, venting, and burning. Knowledge of both methane flow and temperature at the flare stack can define when methane is released into the environment instead of combusted. To accurately determine methane production levels and flow, a pressure transducer can detect both pressure and vacuum present within the methane production system.

Thermocouples connected to wireless I/O devices create the wireless sensor network that detects the heat of an active flame, verifying that methane is burning. Logically, if the meter is indicating a methane flow and the temperature at the flare stack is high, then the methane is burning correctly. If the meter indicates methane flow and the temperature is low, methane is releasing into the environment. ^[7]

Vehicle Detection

Wireless sensor networks can use a range of sensors to detect the presence of vehicles ranging from motorcycles to train cars.

Agriculture

Using wireless sensor networks within the agricultural industry is increasingly common. Gravity fed water systems can be monitoring using pressure transmitters to monitor water tank levels, pumps can be controlled using wireless I/O devices, and water use can be measured and wirelessly transmitted back to a central control center for billing. Irrigation automation enables more efficient water use and reduces waste.

Windrow Composting

Composting is the aerobic decomposition of biodegradable organic matter to produce compost, a nutrient-rich mulch of organic soil produced using food, wood, manure, and/or other organic material. One of the primary methods of composting involves using windrows.

To ensure efficient and effective composting, the temperatures of the windrows must be measured and logged constantly. With accurate temperature measurements, facility managers can determine the optimum time to turn the windrows for quicker compost production. Manually collecting data is time consuming, cannot be done continually, and may expose the person collecting the data to harmful pathogens. Automatically collecting the data and wirelessly transmitting the data back to a centralized location allows composting temperatures to be continually recorded and logged, improving efficiency, reducing the time needed to complete a composting cycle, and minimizing human exposure and potential risk.

An industrial wireless I/O device mounted on a stake with two thermocouples, each at different depths, can automatically monitor the temperature at two depths within a compost windrow or stack. Temperature sensor readings are wirelessly transmitted back to the gateway or host system for data collection, analysis, and logging. Because the temperatures are measured and recorded continuously, the composting rows can be turned as soon as the temperature reaches the ideal point. Continuously monitoring the temperature may also provide an early warning to potential fire hazards by notifying personnel when temperatures exceed recommended ranges. ^[7]

Greenhouse Monitoring

Wireless sensor networks are also used to control the temperature and humidity levels inside commercial greenhouses. When the temperature and humidity drops below specific levels, the greenhouse manager must be notified via e-mail or cell phone text message, or host systems can trigger misting systems, open vents, turn on fans, or control a wide variety of system responses. Because some wireless sensor networks are easy to install, they are also easy to move as the needs of the application change. ^[7]

Characteristics

Unique characteristics of a WSN include:

Limited power they can harvest or store
Ability to withstand harsh environmental conditions
Ability to cope with node failures
Mobility of nodes
Dynamic network topology
Communication failures
Heterogeneity of nodes
Large scale of deployment
Unattended operation
Node capacity is scalable,only limited by bandwidth of gateway node.

Sensor nodes can be imagined as small computers, extremely basic in terms of their interfaces and their components. They usually consist of a processing unit with limited computational power and limited memory, sensors (including specific conditioning circuitry), a communication device (usually radio transceivers or alternatively optical), and a power source usually in the form of a battery. Other possible inclusions are energy harvesting modules, secondary ASICs, and possibly secondary communication devices (e.g. RS-232 or USB).

The base stations are one or more distinguished components of the WSN with much more computational, energy and communication resources. They act as a gateway between sensor nodes and the end user.

Platforms

2009-08-19T09:16:00.000-07:00

Standards

Several standards are currently either ratified or under development for wireless sensor networks. ZigBee is a proprietary mesh-networking specification intended for uses such as embedded sensing, medical data collection, consumer devices like television remote controls, and home automation. Zigbee is promoted by a large consortium of industry players. WirelessHART is an extension of the HART Protocol and is specifically designed for Industrial applications like Process Monitoring and Control. WirelessHART was added to the overall HART protocol suite as part of the HART 7 Specification, which was approved by the HART Communication Foundation in June 2007^[8]. 6LoWPAN ^[9] is the IETF standards track specification for the IP-to-MAC-Layer mapping for IPv6 on IEEE 802.15.4. ISA100 is a new standard under development that makes use of 6lowpan and provides additional agreements for industrial control applications^{[citation needed]}. ISA100 is scheduled for completion in 2009. ZigBee, WirelessHART, and 6lowpan/ISA100 all are based on the same underlying radio standard: IEEE 802.15.4 - 2006.

Also relevant to sensor networks is the emerging IEEE 1451 which attempts to create standards for the smart sensor market. The main point of smart sensors is to move the processing intelligence closer to the sensing device. ^[10]

Hardware

Main article: sensor node

The main challenge is to produce low cost and tiny sensor nodes. With respect to these objectives, current sensor nodes are mainly prototypes. Miniaturization and low cost are understood to follow from recent and future progress in the fields of MEMS and NEMS. Some of the existing sensor nodes are given below. Some of the nodes are still in research stage.

An overview of commonly used sensor network platforms, components, technology and related topics is available in the SNM - Sensor Network Museum^tm.

Software

Energy is the scarcest resource of WSN nodes, and it determines the lifetime of WSNs. WSNs are meant to be deployed in large numbers in various environments, including remote and hostile regions, with ad-hoc communications as key. For this reason, algorithms and protocols need to address the following issues:

Lifetime maximization
Robustness and fault tolerance
Self-configuration

Some of the "hot" topics in WSN software research are:

Security
Mobility (when sensor nodes or base stations are moving)
Middleware: the design of middle-level primitives between the software and the hardware

Operating systems

Operating systems for wireless sensor network nodes are typically less complex than general-purpose operating systems both because of the special requirements of sensor network applications and because of the resource constraints in sensor network hardware platforms. For example, sensor network applications are usually not interactive in the same way as applications for PCs. Because of this, the operating system does not need to include support for user interfaces. Furthermore, the resource constraints in terms of memory and memory mapping hardware support make mechanisms such as virtual memory either unnecessary or impossible to implement.

Wireless sensor network hardware is not different from traditional embedded systems and it is therefore possible to use embedded operating systems such as eCos or uC/OS for sensor networks. However, such operating systems are often designed with real-time properties. Unlike traditional embedded operating systems, however, operating systems specifically targeting sensor networks often do not have real-time support.

TinyOS^[11] is perhaps the first^{[citation needed]} operating system specifically designed for wireless sensor networks. Unlike most other operating systems, TinyOS is based on an event-driven programming model instead of multithreading. TinyOS programs are composed into event handlers and tasks with run to completion-semantics. When an external event occurs, such as an incoming data packet or a sensor reading, TinyOS calls the appropriate event handler to handle the event. Event handlers can post tasks that are scheduled by the TinyOS kernel some time later. Both the TinyOS system and programs written for TinyOS are written in a special programming language called nesC which is an extension to the C programming language. NesC is designed to detect race conditions between tasks and event handlers.

There are also operating systems that allow programming in C. Examples of such operating systems include Contiki,^[12] MANTIS,^[13] BTnut,^[14] SOS^[15] and Nano-RK.^[16] Contiki is designed to support loading modules over the network and supports run-time loading of standard ELF files.^[17] The Contiki kernel is event-driven, like TinyOS, but the system supports multithreading on a per-application basis.^[18] Furthermore, Contiki includes protothreads that provide a thread-like programming abstraction but with a very small memory overhead.^[19] Unlike the event-driven Contiki kernel, the MANTIS and Nano-RK kernels are based on preemptive multithreading.^[20]^[21] With preemptive multithreading, applications do not need to explicitly yield the microprocessor to other processes. Instead, the kernel divides the time between the active processes and decides which process that currently can be run which makes application programming easier. Nano-RK is a real-time resource kernel that allows fine grained control of the way tasks get access to CPU time, networking and sensors. Like TinyOS and Contiki, SOS is an event-driven operating system.^[22] The prime feature of SOS is its support for loadable modules. A complete system is built from smaller modules, possibly at run-time. To support the inherent dynamism in its module interface, SOS also focuses on support for dynamic memory management.^[23] BTnut^[24] is based on cooperative multi-threading and plain C code, and is packaged with a developer kit and tutorial^[25]

LiteOS is a newly developed OS for wireless sensor networks, which provides UNIX like abstraction and support for C programming language. ^[26]

Middleware

There is considerable research effort currently invested in the design of middleware for WSN's.^[3] In general approaches can be classified into distributed database, mobile agents, and event-based.^[27]

Programming languages

Programming the sensor nodes is difficult when compared with normal computer systems. The resource constrained nature of these nodes gives rise to new programming models although most nodes are currently programmed in C.

c@t (Computation at a point in space (@) Time)
DCL (Distributed Compositional Language)
galsC
LabVIEW
nesC
Protothreads
SNACK
SNAPpy (Python)
SQTL
Java Sun SPOT
uSWN

Algorithms

WSNs are composed of a large number of sensor nodes, therefore, an algorithm for a WSN is implicitly a distributed algorithm. In WSNs the scarcest resource is energy, and one of the most energy-expensive operations are data transmission and idle listening. For this reason, algorithmic research in WSN mostly focuses on the study and design of energy aware algorithms for saving energy by reducing the amount of data being transmitted - using techniques like data aggregation -, changing the transmission power of the sensor nodes or turning nodes off while preserving connectivity and coverage - applying Topology control algorithms -.

Another characteristic to take into account is that due to the constrained radio transmission range and the polynomial growth in the energy-cost of radio transmission with respect to the transmission distance, it is very unlikely that every node will reach the base station, so data transmission is usually multi-hop (from node to node, towards the base stations).

The algorithmic approach to WSN differentiates itself from the protocol approach by the fact that the mathematical models used are more abstract, more general, but sometimes less realistic than the models used for protocol design.

Simulators

There are network simulator platforms specifically designed to model and simulate Wireless Sensor Networks, like TOSSIM, which is a part of TinyOS and COOJA which is a part of Contiki. Traditional network simulators like ns-2 have also been used. A platform independent component based simulator with wireless sensor network framework, J-Sim (www.j-sim.org) can also be used. In addition, there is a simulator focused on the evaluation of topology control protocols in WSNs called Atarraya. An extensive list of simulation tools for Wireless Sensor Networks can be found at the CRUISE WSN Simulation Tool Knowledgebase.

Based on the OMNeT++ network simulator architecture, Mobility Framework and Castalia can be used for simulation of wireless sensor networks.

Based on Matlab, the Prowler (Probabilistic Wireless Network Simulator) toolbox is available. JProwler is a version of Prowler written in Java.

Data visualization

The data gathered from wireless sensor networks is usually saved in the form of numerical data in a central base station. Additionally, the Open Geospatial Consortium (OGC) is specifying standards for interoperability interfaces and metadata encodings that enable real time integration of heterogeneous sensor webs into the Internet, allowing any individual to monitor or control Wireless Sensor Networks through a Web Browser. There are several techniques to retrieve data from the nodes, some of the protocols rely on flooding mechanisms, other map the data to nodes by applying the concept of DHT^[28] ^[29].

Information Fusion

In wireless sensor networks, information fusion, also called data fusion, has been developed for processing sensor data by filtering, aggregating, and making inferences about the gathered data. Information fusion deals with the combination of multiple sources to obtain improved information: cheaper, greater quality or greater relevance^[5]. Within the wireless sensor networks domain, simple aggregation techniques such as maximum, minimum, and average, have been developed for reducing the overall data traffic to save energy.^[30]

New Wireless Technology Creating Waves

2009-08-16T11:41:00.000-07:00

Rice University's Center for Multimedia Communication (CMC) recently disclosed that its new technology called Wireless open-Access Research Platform, also referred to as WARP, allegedly transfers data 100 times faster than 3G Networks (100x3G) and has many big players in different industries showing keen interest, buying the development kits, and are using them for innovative futuristic technological solutions.

WARP enables agencies to work on substituting and replacing existing technologies, and even experiment with hitherto unexplored zones. For example, NASA is working on a concept to reduce weight, cost and complexity of the wiring for communications and signals in spaceships,Motorola ( News - Alert) is developing a Greenfield project for wireless internet to reach rural India by creating low cost architecture and Toyota is attempting to avoid collisions between vehicles and enhance overall traffic management by testing car-to-car communications.

Invariably, the ultimate acceptance test for any new technology is when it is taken as a given, modified and adapted to sculpt something different. Some companies were discovered to be dismantling and reassembling the hardware with additions, and maybe subtractions, to create new functions. "That's one of the best things about WARP. It is going to lead to innovations that we could never have anticipated. ," saidAshutosh Sabharwal ( News - Alert), Director of CMC.

WARP is a flexible tool developed with expansion feasibility for wireless researchers to qualify and quantify their ideas by writing programs which then transform WARP boards functionally into the newly conceived types of wireless device – maybe transmitters, or routers, or access points, to name a few. Physically, at first glance it looks like a middle to late 90’s computer motherboard with a few satellite boards in attendance. Up close it’s a high end adaptable rig with a Virtex-II Pro(cessor) from XILINX, an arsenal of transmitters and other related wireless devices.

The manufacturing, sales and support for all the different types of WARP boards, for Field-Programmable Gate Array (FPGA), Radio, Clock and Analog, and kits, for Multiple-input and multiple-output (MIMO) and single-input and single-output systems (SISP), are being managed by Mango Communications.

A futuristic Cognitive Radio (CR) WARP prototype is on the cards. Cognitive Radio attempts to intelligently adapt its settings to log on to the best frequency available to deliver peerless digital exchanges. Majorly untapped frequencies worldwide to be utilized on the RF spectrum for this purpose are the ones used for amateur radio and paging, that is in the range of 30MHz to 300MHz.

WARP has funding from the National Science Foundation till 2010. Incidentally, within the first year of funding, WARP prototypes were ready for delivery. Now with enquiries, feed-back, and order positions looking up, further funding, either for more research or for business, should not be a bother.

There have been in existence different and well known expansions and uses for WARP, or Warp, in the field of IT. IBM (News - Alert) released an Operating System (OS) in 1994 called OS/2 version 3.0, or Warp. APT International developed software called WARP to improve IBM’s Mainframe performance. Cypress Semiconductor worked on a project called Warp for developing a low cost Hardware Description Language (HDL) for Very High Speed Integrated Circuits (VHSIC’s) – shortened to VHDL – to be used in Complex ProgrammableLogic Devices ( News - Alert) (CPLD’s). Microsoft developed, as a part of Windows 7, a Windows Advanced Rasterization Platform (WARP) for its Multimedia and games application programming interfaces (APIs), which are collectively known as Direct X. In the IT wing of security systems Warning, Advice and Reporting Points are known as WARP’s. In Single Instruction Multiple Thread (SIMT) architecture, Warp is a type of multithreading.

Technorati

2009-07-25T18:23:00.000-07:00

Technorati is an Internet search engine for searching blogs. By June 2008, Technorati indexes 112.8 million blogs and over 250 million pieces of tagged social media. The name Technorati is a portmanteau, pointing to the technological version of literati, or intellectuals.

Technorati was founded by Dave Sifry and its headquarters are in San Francisco, California, USA. Tantek Çelik was the site's Chief Technologist.

Technorati uses and contributes to open source software. Technorati has an active software developer community, many of them from open-source culture. Sifry is a major open-source advocate, and was a founder of LinuxCare and later of Wi-Fi access point software developer Sputnik. Technorati includes a public developer's wiki, where developers and contributors collaborate, also various open APIs.

The site won the SXSW 2006 awards for Best Technical Achievement and also Best of Show. It was also nominated for a 2006 Webby Award for Best Practices, but lost to Flickr and Google Maps.

1 Technology
2 Criticism
3 References
4 External links

Technology

Technorati looks at tags that authors have placed on their websites. These tags help categorize search results, with recent results coming first^{[citation needed]}.

Technorati rates each blog's "authority", the number of unique blogs linking to the blog over the previous six months.

Criticism

In February 2006, Debi Jones pointed out that Technorati's "State of the Blogosphere" postings, which claimed that they track 27.7 million blogs, failed to take into account MySpace blogs, of which she says there are 56 million. As a result, she says the utility of Technorati as a gauge for blog popularity is questionable. However by March 2006, Aaron Brazell pointed out that Technorati had started tracking MySpace blogs.

In May 2006 Technorati teamed up with the PR agency Edelman. The deal earned a lot of criticism, both on principle and as a result of Edelman's 2006 fake blog scandals. Edelman and Technorati officially ended the deal in December 2006. That month, Oliver Reichenstein pointed out that the so called "State of the Blogosphere" was more of a PR-tool and money maker for Edelman and Technorati than a reliable source, explaining in particular a) why Technorati/Edelman's claim that "31% of the blogs are written in Japanese" was "bogus" and b) where the financial profit for the involved parties was in this.

In May 2007, Andrew Orlowski writing for the tech tabloid The Register criticized Technorati's May 2007 redesign. He suggests that Technorati has decided to focus more on returning image thumbnails rather than blog results. He also claims that Technorati never quite worked correctly in the past and that the alleged refocus is "a tacit admission that it's given up on its original mission".

The Gap Grows Wider: MySpace Eats Facebook’s Dust In The U.S.

2009-07-14T05:21:00.000-07:00

The gap between Facebook and MySpace is growing wider in the U.S. In May, Facebook finally caught up to MySpace in unique U.S. visitors and surpassed its rival social network by a smidgeon. Last month, Facebook left MySpace in the dust, according to June data from comScore. Facebook reached 77 million unique visitors for the month of June, rising from 70.28 million unique visitors in May. MySpace had 68.4 million unique visitors in June, dropping from 70.25 million unique visitors in May.

Facebook is steadily growing in the U.S.; the network gained just under 7 million unique visitors in June compared to a gain of 2.8 million U.S. unique visitors in May. In comparison, MySpace lost nearly 4 million unique visitors in June, compared to 700,000 unique visitors lost in May. While Facebook is growing both in the U.S. and internationally, MySpace appears to be stagnating.

The widening of the gap between the rival social networks network comes at a time when MySpace is under new management and recently terminated two-thirds of its international staff, laying off staff in countries where MySpace is being trounced by Facebook. MySpace’s international numbers were startling. For example, in India, where social networking is growing fast, Facebook had 6.4 million unique visitors in May, compared to 848,000 unique visitors to MySpace. In our most recent model of the true value of social networks, MySpace fell below Facebook, dropping from the top spot last year.

MySpace still generates more page views than Facebook. In June, MySpace had 32.4 billion page views in the U.S., but that number dropped 10 percent in a single month, from May (gulp). Facebook is catching up there as well, with 21.3 billion page views in June, a 12 percent increase from May. And worldwide, Facebook is already ahead. As we reported a few months ago, worldwide monthly page views for MySpace declined from 47.4 billion a year ago to 38 billion in April, a 20% drop. In that same period Facebook grew from 44 billion to 87 billion, a roughly 100% increase. MySpace’s user number growth has stalled out also, and developers are reporting that activity on MySpace is decreasing at a dramatic rate, as high as “half a percent a week.”

A decline in user numbers isn’t the only predicament that MySpace is in—there is speculation that the social network could lose one of its major revenue streams in the near future. In 2010, MySpace will be receiving its last “welfare payment” from Google (stemming from an advertising deal between News Corp. and Google struck in 2006), after which it looks like it will be cut off. Under the terms of the agreement, MySpace will receive $300 million over the next year if the network hits certain search pageview requirements, which considering the recent data on page views, may not happen.

Meanwhile, Facebook shows no signs of slowing down. The network successfully launched its “vanity URLs,” in June, with millions of users signing up for the new feature within days. The network also got some notice around its use during the Iran elections and protests, and around the new privacy settings surrounding its “Everyone” button.