1. ABSTRACT

Wireless sensor networks (WSNs) enable new applications and require non-conventional paradigms for protocol design due to several constraints. Owing to the requirement for low device complexity together with low energy consumption (i.e., long network lifetime), a proper balance between communication and signal/data processing Capabilities must be found. This motivates a huge effort in research activities, standardization process, and industrial investments on this field since the last decade. This survey paper aims at reporting an overview of WSNs technologies, main applications and standards, features in WSNs design, and evolutions. In particular, some peculiar applications, such as those based on environmental monitoring, are discussed and design strategies highlighted; a case study based on a real implementation is also reported. Trends and possible evolutions are traced. Emphasis is given to the IEEE 802.15.4 technology, which enables many applications of WSNs. Some example of performance characteristics of 802.15.4-based networks are shown and discussed as a function of the size of the WSN and the data type to be exchanged among nodes.

1

2. INTRODUCTION

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.

2.1 WSN TECHNOLOGY

The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning "motes" of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of dollars, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth. The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.

2.2 HISTORY

The origins of the research on WSNs can be traced back to the Distributed Sensor Networks(DSN) program at the Defense Advanced Research Projects Agency (DARPA) at around 1980. By this time, the ARPANET (Advanced Research Projects Agency Network) had been operational for a number of years, with about 200 hosts at universities and research institutes. DSNs were assumed to have many spatially distributed low-cost sensing nodes that collaborated with each other but operated autonomously, with information being routed to whichever node was best able to use the information. At that time, this was actually an ambitious program. There were no personal computers and workstations; processing was mainly performed on minicomputers and the Ethernet was just becoming popular. Technology components for a DSN were identified in a Distributed Sensor Nets workshop in 1978 (Proceedings of the Distributed Sensor Nets Workshop, 1978). these included sensors (acoustic), communication and processing modules, and distributed software. Researchers at Carnegie Mellon University (CMU) even developed a communication-oriented operating system called Accent (Rashid & Robertson, 1981), which allowed flexible, transparent access to distributed resources

2

required for a fault-tolerant DSN. A demonstrative application of DSN was a helicopter tracking system (Myers et al., 1984), using

a distributed array of acoustic microphones by means of signal abstractions and matching techniques, developed at the Massachusetts Institute of Technology (MIT). Even though early researchers on sensor networks had in mind the vision of a DSN, the technology was not quite ready. More specifically, the sensors were rather large and This work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Program and is part of the MELODY Project, which is funded by the Research Council of Norway under the contract number 187857/S10.

In the new wave of sensor network research, networking techniques and networked information processing suitable for highly dynamic ad hoc environments and resource constrained sensor nodes have been the focus. Further, the sensor nodes have been much smaller in size (i.e. pack of cards to dust particle) and much cheaper in price, and thus many new civilian applications of sensor networks such as environment monitoring, vehicular sensor network and body sensor network have emerged. Again, DARPA acted as a pioneer in the new wave of sensor network research by launching an initiative research program called SensIT. Which provided the present sensor networks with new capabilities such as ad hoc networking, dynamic querying and tasking, reprogramming and multitasking. At the same time, the IEEE noticed the low expense and high capabilities that sensor networks offer. The organization has defined the IEEE 802.15.4 standard (IEEE 802.15 WPAN Task Group 4, n.d.) for low data rate wireless personal area networks. Based on IEEE 802.15.4, ZigBee Alliance (ZigBee Alliance, n.d.) has published the ZigBee standard which specifies a suite of high level communication protocols which can be used by WSNs. Currently, WSN has been viewed as one of the most important technologies for the 21st century (21 Ideas for the 21st Century,1999). Countries such as China have involved WSNs in their national strategic research programmer’s (Ni, 2008). The commercialization’s of WSNs are also being accelerated by new formed companies like Crossbow Technology (Crossbow Technology, n.d.) and Dust Networks

2.3 WSN ARCHITECTURE

The architecture of wsn consist of sensor microcontroller unit antenna and transmitter & receiver of the system.sens0r and control units are connected to the battery for required power supply voltage sensor sense the physical environment and send the input to the A/D converter to convert it into digital form and then it is send to the control unit of microcontroller from where the o/p’s are controlled by the mechanism stored in microcontroller unit

3

The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.

Typical multi-hop wireless sensor network architecture

4

3. SENSOR

Sensors are the very important part of any sensor network it is the primary hub of wireless sensor networks. All wireless technology is depend upon these sensors in our general life we use many sensors ,do u know that how much sensors are working in your system or in your mobile cell. U can not think about a network without sensor

3.1 DEFINITIONA 'sensor' is a device that measures a physical quantity and converts it into a 'signal' which can be read by an observer or by an instrument. For example, a mercury thermometer converts the measured temperature into the expansion and contraction of a liquid which can be read on a calibrated glass tube.

3.2 TYPE OF SENSOR

There are a lot of different types of sensors. Sensors are used in everyday objects.

Thermal sensors

A sensor that detects temperature. Thermal sensors are found in many laptops and computers in order to sound an alarm when a certain temperature has been exceeded.

temperature sensors: thermometers

heat sensors: bolometer, calorimeter

Electromagnetic sensors

An electronic device used to measure a physical quantity such as pressure or loudness and convert it into an electronic signal of some kind (e.g. a voltage).

5

electrical resistance sensors: ohmmeter

electrical voltage sensors: voltmeter electrical power sensors: watt-hour meter magnetism sensors: magnetic compass metal detectors Radar

Mechanical sensors

Pressure sensors: barometer

Vibration and shock sensors

Motion sensors

A motion sensor detects physical movement in a given area.

radar gun, tachometer

Car sensors

reversing sensor

rain sensor

3.3 The trend of sensors

Because of certain disadvantages of physical contact sensors, newer technology non-contact sensors have become prevalent in industry, performing well in many applications. The recent style of non-contact sensors shows that “Thin (g) is In”. Market trends show that form and size are important. Users are looking for smaller and more accurate sensors. New technologies for the sensing chips are breaking application barriers. For the future, the trend will be to continue to provide smaller, more affordable sensors that have the flexibility to fit even more applications in both industrial and commercial environments.

6

4. FEATURES:

In spite of the diverse applications, sensor networks pose a number of unique technical features due to the following factors:

4.1. Ad hoc deployment:Most sensor nodes are deployed in regions which have no infrastructure at all. A typical way of deployment in a forest would be tossing the sensor nodes from an aeroplane. In such a situation, it is up to the nodes to identify its connectivity and distribution.

4.2 .Unattended operation: In most cases, once deployed, sensor networks have no human intervention. Hence the nodes themselves are responsible for reconfiguration in case of any changes.

4.3. Unmetered: The sensor nodes are not connected to any energy source. There is only a finite source Of energy, which must be optimally used for processing and communication? An interesting fact is That communication dominates processing in energy consumption. Thus, in order to make optimal Use of energy, communication should be minimized as much as possible.

4.4 Dynamic changes: It is required that a sensor network system be adaptable to changing Connectivity (for e.g., due to addition of more nodes, failure of nodes etc.) as well as changing Environmental stimuli. Thus, unlike traditional networks, where the focus is on maximizing channel throughput or minimizing node deployment, the major consideration in a sensor network is to extend the system lifetime as well as the system robustness.

5. Routing Protocols for WSNs

Flooding

Flooding is an old routing mechanism that may also be used in sensor networks. In Flooding, a node sends out the received data or the management packets to its neighbors by broadcasting, unless a maximum number of hops for that packet are reached or the destination of the packets is arrived. here are some deficiencies for this routing technique [ Implosion: is the case where a duplicated data or packets are sent to the same node. Overlap: if two sensor nodes cover an overlapping measuring region, both of them will sense/detect the same data. As a result, their neighbor nodes will receive duplicated data

7

or messages. Resource blindness: A WSN protocol must be energy resource-aware and adapts its sensing, communication and computation to the state of its energy.

GossipingGossiping protocol is an alternative to flooding mechanism. In Gossiping, nodes can forward the incoming data/packets to randomly selected neighbor node. Once a gossiping node receives the messages, it can forward the data back to that neighbor or to another one randomly selected neighbor node. This technique assists in energy conservation by randomization. Gossiping can solve the implosion problem.

SPINSPIN (Sensor Protocols for Information via Negotiation) is a family of adaptive protocols for WSNs. Their design goal is to avoid the drawbacks of flooding protocols mentioned above by utilizing data negotiation and resource-adaptive algorithms.

Directed di_usionDirected di_usion is another data dissemination and aggregation protocol. It is a data-centric and application aware routing protocol for WSNs. It aims at naming all data generated by sensor nodes by attribute-value pairs.

LEACHLEACH (Low Energy Adaptive Clustering Hierarchy) is a self-organizing, adaptive clustering-based protocol that uses randomized rotation of cluster-heads to evenly distribute the energy load among the sensor nodes in the network

PEGASISPEGASIS (Power-E_client GAthering in Sensor Information Systems) is a greedy chain-based power e_cient algorithm.

The key features of PEGASIS are The BS is fixed at a far distance from the sensor nodes. The sensor nodes are homogeneous and energy constrained with uniform energy. No mobility of sensor nodes.

GEARGEAR (Geographical and Energy Aware Routing) is a recursive data dissemination protocol WSNs. It uses energy aware and geographically informed neighbor selection Heuristics to rout a packet to the targeted region

6. APPLICATIONSThe original motivation behind the research into WSNs was military application. Examples of military sensor networks include large-scale acoustic ocean surveillance systems for the detection of submarines, self-organized and randomly deployedWSNs for battlefield surveillance and attaching microsensors to weapons for stockpile surveillance (Pister, 2000). As the costs for sensor nodes and communication networks have been

8

reduced, many other potential applications including those for civilian purposes have emerged. The following are a few examples.

Environmental MonitoringEnvironmental monitoring (Steere et al., 2000) can be used for animal tracking, forest surveillance, flood detection, and weather forecasting. It is a natural candidate for applying WSNs, because the variables to be monitored, e.g. temperature, are usually distributed over a large region. One example is that researchers from the University of Southampton have built a glacial environment monitoring system using WSNs in Norway (Martinez et al., 2005). They collect data from sensor nodes installed within the ice and the sub-glacial sediment without the use of wires which could disturb the environment.

9

Health MonitoringWSNs can be embedded into a hospital building to track and monitor patients and all medical resources. Special kinds of sensors which can measure blood pressure, body temperature and electrocardiograph (ECG) can even be knitted into clothes to provide remote nursing for the elderly. When the sensors are worn or implanted for healthcare purposes, they form a special kind of sensor network called a body sensor network (BSN). BSN is a rich interdisciplinary area which revolutionizes the healthcare system by allowing inexpensive, continuousand ambulatory health monitoring with real-time updates of medical records via the Internet.

TRAFFIC CONTROLSensor networks have been used for vehicle traffic monitoring and control for some time. At many crossroads, there are either overhead or buried sensors to detect vehicles and to control the traffic lights. Furthermore, video cameras are also frequently used to monitor road segments with heavy traffic. However, the traditional communication networks used to connect these sensors are costly, and thus traffic monitoring is usually only available at a few critical points in a city (Chong & Kumar, 2003). WSNs will completely change the landscape of traffic monitoring and control by installing cheap sensor nodes in the car, at

10

the parking lots, along the roadside, etc. Street line, Inc. (Street line, Inc., n.d.) is a company which uses sensor network technology to help drivers find unoccupied parking places and avoid traffic jams. The solutions provided by Street line can significantly improve the city traffic management and reduce the emission of carbon dioxide.

SMART BUILDINGSThe New York Times Building - a Smart Building The headquarters of the New York Times is an example of how different smart building technologies can be combined to reduce energy consumption and to increase user comfort. Overall, the building consumes 30% less energy than traditional office skyscrapers. Opened in November 2007 and designed by Renzo Piano, the building has a curtain wall which serves as a sunscreen and changes color during the day. This wall consists of ceramic rods, “a supporting structure for the screen and an insulated window unit” (Hart, 2008).

The building is further equipped with lighting and shading control systems based on ICT technologies. The lighting system ensures that electrical light is only used when required. Further day lighting measures include a garden in the centre of the ground floor which is open to the sky as well as a large area skylight. The electrical ballasts in the lighting system are equipped with chips that allow each ballast to be controlled separately. The shading system tracks the position of the sun and relies on a sensor network to automatically actuate the raising and lowering of the shades. The high-tech HVAC system is equipped with sensors that measure the temperature. It is further able to rely on free air cooling, i.e. fresh air on cool mornings is brought into the HVAC system. An automated building system monitors in parallel “the air conditioning, water cooling, heating, fire alarm, and generation systems” (Siemens, 2008). The system relies on a large-scale sensor network composed of different kinds of sensors which deliver real-time information. Consequently, energy can be saved as only as few systems are turned on as needed.

11

SecurityWhile the future of WSNs is very prospective, WSNs will not be successfully deployed if security, dependability and privacy issues are not addressed adequately. These issues become more important because WSNs are usually used for very critical applications. Furthermore, WSNs are very vulnerable and thus attractive to attacks because of their limited prices andhuman-unattended deployment .IT provide kee management, authentication, intrusion detection, privacy protection which makes WSN secure.

Air pollution Monitoring:Wireless sensor networks have been deployed in several cities (Stockholm, London or Brisbane) to monitor the concentration of dangerous gases for citizens. These can take advantage of the ad-hoc wireless links rather than wired installations, which also make them more mobile for testing readings in different areas. There are various architectures that can be used for such applications as well as different kinds of data analysis and data mining that can be conducted.

Forest fire Detection:A network of Sensor Nodes can be installed in a forest to detect when a fire has started. The nodes can be equipped with sensors to measure temperature, humidity and gases which are produced by fire in the trees or vegetation. The early detection is crucial for a successful action of the firefighters; thanks to Wireless Sensor Networks, the fire brigade will be able to know when a fire is started and how it is spreading.

Landslide Detection:A landslide detection system, makes use of a wireless sensor network to detect the slight movements of soil and changes in various parameters that may occur before or during a landslide. Through the data gathered it may be possible to know the occurrence of landslides long before it actually happens.

Water Quality Monitoring:Water quality monitoring involves analyzing water properties in dams, rivers, lakes & oceans, as well as underground water reserves. The use of many wireless distributed sensors enables the creation of a more accurate map of the water status, and allows the permanent deployment of monitoring stations in locations of difficult access, without the need of manual data retrieval.

7. Operating systems used in WSN

A WSN typically consists of hundreds or thousands of sensor nodes. These nodes have the capability to communicate with each other using multi-hop communication. Typical applications of these WSN include but not limited to monitoring, tracking, and controlling.

The basic functionality of an operating system is to hide the low-level details of the sensor node by providing a clear interface to the external world. Processor management,

12

memory management, device management, scheduling policies, multi-threading, and multitasking are some of the LowLevel services to be provided by an operating system.

In addition to the services mentioned above, the operating system should also provide services like support for dynamic loading and unloading of modules, providing proper concurrency mechanisms, Application Programming Interface (API) to access underlying hardware, and enforce proper power management policies.

TinyOS:TinyOS is an open source, flexible, component based, and application-specific operating system designed for sensor networks. TinyOS can support concurrent programs with very low memory requirements. The OS has a footprint that fits in 400 bytes. The TinyOS component library includes network protocols, distributed services, sensor drivers, and data acquisition tools.

Contiki OS:Contiki is a lightweight open source OS written in C for WSN sensor nodes. Contiki is a highly portable OS and it is build around an event-driven kernel. Contiki provides preemptive multitasking that can be used at the individual process level. A typical Contiki configuration consumes 2 kilobytes of RAM and 40 kilobytes of ROM. A full Contiki installation includes features like: multitasking kernel, preemptive multithreading, proto-threads, TCP/IP networking, IPv6, a Graphical User Interface, a web browser, a personal web server, a simple telnet client, a screensaver, and virtual network computing.

MANTIS:The MultimodAl system for NeTworks of In-situ wireless Sensors (MANTIS) provides a new multithreaded operating system for WSNs. MANTIS is a lightweight and energy efficient operating system. It has a footprint of 500 bytes, which includes kernel, scheduler, and network stack. The MANTIS Operating System (MOS) key feature is that it is portable across multiple platforms, i.e., we can test MOS applications on a PDA or a PC. Afterwards, the application can be ported to the sensor node. MOS also supports remote management of sensor nodes through dynamic programming. MOS is written in C and it supports application development in C.

Nano-RK: Nano-RK is a fixed, preemptive multitasking real-time OS for WSNs. The design goals for Nano-RK are multitasking, support for multi-hop networking, support for priority-based scheduling, timeliness and schedulability, extended WSN lifetime, application resource usage limits, and small footprint. Nano-RK uses 2 Kb of RAM and 18 Kb of ROM. Nano-RK provides support for CPU, sensors, and network bandwidth reservations. Nano-RK supports hard and soft real-time applications by the means of different real-time scheduling algorithms, e.g., rate monotonic scheduling and rate harmonized scheduling. Nano-RK provides networking support through socket-like abstraction. Nano-RK supports FireFly and MicaZ sensing platforms.

13

Lite OS:LiteOS is a Unix-like operating system designed for WSNs at the University of Illinois at Urbana-Champaign. The motivations behind the design of a new OS for WSN are to provide a Unix-like OS for WSN, provide system programmers with a familiar programming paradigm (thread-based programming mode, although it provides support to register event handlers using callbacks), a hierarchical file system, support for object-oriented programming in the form of LiteC++, and a Unix-like shell. The footprint of LiteOS is small enough to run on MicaZ nodes having an 8 MHz CPU, 128 bytes of program flash, and 4 Kbytes of RAM. LiteOS is primarily composed of three components: LiteShell, LiteFS, and the Kernel.

EPOS:EPOS (Embedded Parallel Operating System) is a component-based framework for the generation of dedicated runtime support environments. The EPOS system framework allows programmers to develop platform-independent applications and analysis tools allow components to be automatically adapted to fulfill the requirements of these particular applications. By definition, one instance of the system aggregates all the necessary support for its dedicated application and nothing else.

Table 1. Operating Systems Summary

Architecture

Programming model

Scheduling

Memory Management and Protection

Communication Protocol Support

Resource Sharing

Support for Real-time Applications

TinyOS Monolithic Primarily event Driven, support for TOS threads has been added

FIFO Static Memory Management with memory protection

Active Message

Virtualization and Completion Events

No

Contiki Modular Protothreads and events

Events are fired as they occur. Interrupts execute w.r.t. priority

Dynamic memory management and linking. No process address space protection.

uIP and Rime

Serialized Access

No

14

MANTIS

Layered Threads Five priority classes and further priorities in each priority class.

Dynamic memory management supported but use is discouraged, no memory protection.

At Kernel Level COMM layer. Networking Layer is at user level. Application is free to use custom routing protocols.

Through Semaphores.

To some extent at process scheduling level (Implementation of priority scheduling within different processes types)

Nano-RK

Monolithic Threads Rate Monotonic and rate harmonized scheduling

Static Memory Management and No memory protection

Socket like abstraction for networking

Serialized access through mutexes and semaphores. Provide an implementation of Priority Ceiling Algorithm for priority inversion.

Yes

LiteOS Modular Threads and Events

Priority based Round Robin Scheduling

Dynamic memory management and it provides memory protection to processes.

File based communication

Through synchronization primitives

No

8. WSN Architecture:

15

Transport layer:This layer is specifically needed when a system is organized to access other networks. Providing a reliable hop by hop is more energy efficient than end to end. Other protocol used in this layer is STCP (Sensor Transmission Control Protocol) PORT (Price-Oriented Reliable Transport Protocol) PSFQ (pump slow fetch quick).

Network layer:The major function of this layer is routing. This layer has a lot of challenges depending on the application but apparently, the major challenges are in the power saving, limited memory and buffers, sensor does not have a global ID and have to be self organized.

Data link layer:Responsible for multiplexing data streams, data frame detection, MAC, and error control, ensures reliability of point–point or point– multipoint. Errors or unreliability comes from:

Co- channel interference at the MAC layer and this problem is solved by MAC protocols. Multipath fading and shadowing at the physical layer and this problem is solved by

forward error correction (FEC) and automatic repeat request (ARQ).

Physical layer:Can provide an interface to transmit a stream of bits over physical medium. Responsible for frequency selection, carrier frequency generation, signal detection, Modulation and data encryption.

Application layer:Responsible for traffic management and provide software for different applications that translate the data in an understandable form or send queries to obtain certain information.

16

Sensor networks deployed in various applications in different fields, for example; military, medical, environment, agriculture fields.MAC layer:Responsible for Channel access policies, scheduling, buffer management and error control. In WSN we need a MAC protocol to consider energy efficiency, reliability, low access delay and high throughput as major priorities.

9. StandardizationIn the area of WSNs, several standards are currently either ratified or under development. The major standardization bodies are the Institute of Electrical and Electronics Engineers (IEEE), the Internet Engineering Task Force (IETF), the International Society for Automation (ISA) and the HART Communication Foundation, etc. These standardization bodies have different focuses and they provide global, open standards for interoperable, low-power wireless sensor devices. Table 1 provides the comparisons of different standards currently available for the communication protocols of WSNs.

Bluetooth:IEEE 802.15.1 standard, popularly known as Bluetooth, offers moderate data rates at lower energy levels. Due to this, it is ideally suited for high end WSN applications that require higher data rates with harder real time constraints. Bluetooth is used in star topology because of its basic characteristics. Bluetooth devices communicate with each other using set of standard Bluetooth profiles defined by standard body.

ZigBee:IEEE 802.15.4 standard, popularly known as ZigBee, offers low data rates at very low energy levels. Due to this, it is ideally suited for applications requiring infrequent smaller data transfers where battery life is an important issue. However, location estimation based on narrow band DSSS can achieve accuracy only in the order of several meters.

ZigBee coordinator is responsible for managing the network and supervising network formation; ZigBee routers have routing capabilities and they are responsible for linking group of end devices or routers; and ZigBee end devices are simple network end points capable of communicating with other devices in the network.

UWB:Ultra wide band is a technology for transmitting information spread over a large bandwidth (>500 MHz) and it is ideally suited for short distance, high speed communications with very low power budget. As it is based on wide band technology, it can achieve very high geo-location accuracy to the sub-meter levels. UWB provides one of the best options for WSN networking only limited by its shorter range.

Wi-Fi:Wi-Fi represents group of WLAN technologies defined under IEEE 802.11 standard body. In addition to transmission standards like 802.11a/b/g/n, it also includes 802.11s standard for mesh networking. Wi-Fi technologies are capable of providing very high throughput (>100 Mbps) at longer range but required very high power budget. Also, Wi-

17

Fi can locate end point location to the accuracy of several meters only. Because of this limitation, use of Wi-Fi is mostly restricted to devices with fixed power supply.

10. Challenges of WSN:1. Lower speed compared to wired network.

2. Less secure because hacker's laptop can act as access point.

3. More complex to configure than wired network.

4. Can be affected by surrounding's. For example, walls(blocking), Microwave oven (interference), far distance.

5. Sensor node has low battery power, so as battery goes down, nodeGoes down and so does the whole network.

6. Like any other wireless technology, it is easy for hackers to hack WSNs. For most of the applications security & integrity of data is most important hence we have to select networking technology as well as security algorithms accordingly.

7. Due to limited resources and dynamic topology, it is very difficult to design a reliable

routing scheme for WSNs.

8. Quite a few applications like solar energy monitoring, irrigation and air quality

monitoring are associated with harsh environments. Independent of enclosure design,

sensors will be exposed to the outdoor conditions and it is extremely crucial to take

environmental conditions into consideration while designing WSN system.

9. Dynamic topologies and integration with internet affect factors like Quality-of-service

requirements, security, packet errors and variable-link capacity.

10. Energy conservation is a very critical part of WSN because of small battery size.

We need to look at traffic scheduling as well as remote wake-up features to optimize

power consumption.

18

11. ConclusionWSNs have been identified as one of the most prospective technologies in this century. This chapter provides information concerning both its history and current state of the art. In concrete terms, the authors provide an overview about the hardware, software and networking protocol design of this important technology. The authors also discuss the security and ongoing standardization of this technology. Depending on applications, many other techniques such as localization, synchronization and in-network processing can be important, which are not discussed in this chapter.

19