18-04-2013, 03:32 PM
NEW TREND IN WIRELESS SENSOR NETWORKS
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Abstract:
Sensor networks consist of a set of sensor nodes, each equipped with one or more sensors, communication subsystems, storage and processing resources, and in some cases actuators. The sensors in a node observe phenom-ena such as thermal, optic, acoustic, seismic, and acceleration events, while the processing and other components analyze the raw data and formulate answers to specific user requests.
Recent advances in technology have paved the way for the design and implementation of new generations of sensor network nodes, packaged in very small and inexpensive form factors with sophisticated computation and wireless communication abilities. Although still at infancy, these new classes of sensor networks, generally referred to as wireless sensor networks (WSN), show great promise and potential with applications ranging in areas that have already been addressed, to domains never before imagined. In this article we provide an overview of this new and exciting field and a brief discussion on the factors pushing the recent flurry of sensor network related research and commercial undertakings.
INTRODUCTION
Smart environments represent the next evolutionary development step in building, utilities, industrial, home, shipboard, and transportation systems automation. Like any sentient organism, the smart environment relies first and foremost on sensory data from the real world. Sensory data comes from multiple sensors of different modalities in distributed locations. The smart environment needs information about its surroundings as well as about its internal workings; this is captured in biological systems by the distinction between exteroceptors and proprioceptors.
Applications:
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:
It is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. As an example, a large quantity of sensor nodes could be deployed over a battlefield to detect enemy intrusion instead of using landmines. 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 techniques), need for security, etc.
Environmental monitoring:
A number of WSN deployments have been done in the past in the context of environmental monitoring. Many of these have been short lived, often due to the prototypical nature of the projects. A more long-lived deployment is monitoring the state of permafrost in the swiss alps.
Communication Networks:
The study of communication networks can encompass several years at the college or university level. To understand and be able to implement sensor networks, however, several basic primary concepts are sufficient.
Network Topology :
The basic issue in communication networks is the transmission of messages to achieve a prescribed message throughput (Quantity of Service) and Quality of Service (QoS). QoS can be specified in terms of message delay, message due dates, bit error rates, packet loss, economic cost of transmission, transmission power, etc. Depending on QoS, the installation environment, economic considerations, and the application, one of several basic network topologies may be used.
Mesh networks :
These are regularly distributed networks that generally allow transmission only to a node’s nearest neighbors. The nodes in these networks are generally identical, so that mesh nets are also referred to as peer-to-peer (see below) nets. Mesh nets can be good models for large-scale networks of wireless sensors that are distributed over a geographic region, e.g. personnel or vehicle security surveillance systems. Note that the regular structure reflects the communications topology; the actual geographic distribution of the nodes need not be a regular mesh. Since there are generally multiple routing paths between nodes, these nets are robust to failure of individual nodes or links. An advantage of mesh nets is that, although all nodes may be identical and have the same computing and transmission capabilities, certain nodes can be designated as ‘group leaders’ that take on additional functions. If a group leader is disabled, another node can then take over these duties.
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 is data transmission. For this reason, algorithmic research in WSN mostly focuses on the study and design of energy aware algorithms for data transmission from the sensor nodes to the base stations. Data transmission is usually multi-hop (from node to node, towards the base stations), due to the polynomial growth in the energy-cost of radio transmission with respect to the transmission distance.
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
SIGNAL PROCESSING AND DECISION-MAKING :
The figure showing the IEEE 1451 Smart Sensor includes basic blocks for signal conditioning (SC), digital signal processing (DSP), and A/D conversion. Let us briefly mention some of the issues here.
Signal Conditioning
Signals coming from MEMS sensors can be very noisy, of low amplitude, biased, and dependent on secondary parameters such as temperature. Moreover, one may not always be able to measure the quantity of interest, but only a related quantity. Therefore signal conditioning is usually required. SC is performed using electronic circuitry, which may conveniently be built using standard VLSI fabrication techniques in situ with MEMS sensors. A reference for SC, A/D conversion, and filtering is [Lewis 1992].
Conclusion:
In wireless sensor networks, remote updating of the nodes is essential. Because communication is relatively costly in terms of power, many of the approaches discussed in this paper focus on reducing the size of the code. A typical approach is to use a virtual machine or an operating system to accomplish this.