Wireless Sensor Network: Theory & Challenges

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1 Summary Report on Wireless Sensor Network: Theory & Challenges Submitted by Prof.A.R.Deshmukh Prof.P.H.Rangaree

2 The advances in the fields of semiconductor devices and large scale transistor integration coupled with the development of high speed broadband wireless technologies such as MIMO-OFDM have led to the birth of Wireless sensor networks (WSN). Envisioned as the bridge between the modern broadband packet data networks and the physical world, WSNs have made possible real-time data aggregati on and analysis on an unprecedented scale. Naturally, they have attracted the attention and garnered wide spread appeal towards applications in diverse areas such as disaster warning systems, crop/ environment monitoring, health care, safety and strategic areas such as defense reconnaissance, surveillance, intruder detection etc. However, WSNs pose unique challenges towards successful design and implementation of pervasive sensing networks. On the one hand ensuring sensor data integrity over the error prone fading wireless channels is a substantial hindrance, especially in the context of energy constrained wireless sensor nodes. On the other hand, aggregation and analysis of massive data sets is logistically complex in such large scale WSNs. Further, topology management and route discovery hold the key to robust WSN deployment for military applications. WSNs necessitate the development of innovative algorithms for power management, sensor communication, ranging, localization, distributed processing and dynamic routing. Beginning with an introduction to the foundations and background of WSNs, this course is expected to cover the research aspects necessary for WSN deployment. Broadly characterized into the PHY, MAC and Network layers of the standard WSN, the program modules are designed to specialize in individual WSN focus areas such as sensor data fusion, topology optimization, distributed estimation and detection, shortest path routing, optimal power management policies amongst others. A WSN demonstration session towards the end provides practical exposure to WSN technology. The target audience for IIT Kanpur was 1. Practicing wireless system engineers. 2. Graduate Students Pursuing Research in Wireless Communications. 3. Teachers of engineering colleges. The course work was organized by Electrical Engineering Department(IIT Kanpur).The course coordinators were Prof.Adrish Banerjee & Prof.Ketan Rajawat.Further different sessions were conducted by Prof. Aditya Jagannathan,Prof.The training was from th July 2013.The course content was were related to the theoretical aspects of WSN as well as the challenges for research areas in the same field.following are the details which were covered in different sessions.

3 Group photo after completion of course work Data visualization The data gathered from wireless sensor networks is usually saved in the form of numerical data in a central base station. There are many programs, like TosGUI and MonSense,GSN that facilitate the viewing of these large amounts of data. 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. A typical wireless sensor network consists of a base station and several nodes distributed or positioned in the environment of interest. Each node is expected to detect events of interest and estimate parameters that characterize these events. The resulting information at a node needs to be transmitted to the base station either directly or in multihop fashion involving automatic routing through several other nodes in the network. Implementation of such a network requires hardware components and corresponding software modules to program these components in a cooperative manner. A commercial hardware platform that we have been investigating consists of processor cum radio boards commonly referred to as motes. Each mote, a battery-powered device, consists of a sensor unit, a power unit, a two-way ISM band radio transceiver unit (includes an RF antenna), an ADC unit, a processor that runs TinyOS-based code, and logger memory capable of storing up to 100,000 measurements. A base station consists of a mote attached to a mote-interface-board that is interfaced to a PC via the parallel port. Two types of motes in this commercial system [1], namely, mica2dot and mica2 are shown in Figure 1. Included in the product are sensor boards that directly mate with a mote. These boards consist of sensor modules e.g. accelerometer, magnetometer, microphone, photosensor, thermistor, and in addition, allow integration of user s own sensors. In terms of software, the operating system for programming this particular sensor network is called TINYOS. TinyOS is an event-driven operating system designed for sensor network nodes that have very limited resources (e.g. 8K bytes of

4 program memory, 512 bytes of RAM). The programming language used to control and run the hardware components to perform user-defined tasks is called nesc. nesc is an extension to C designed to embody the structuring concepts and execution model of TinyOS. Software is developed and supported by Berkley. Reception and transmission is available at different frequencies. Professional mote development kits available in the market allow frequencies of 315MHz, 433 MHz and 868/916 MHz with typical coverage of 1000ft, 1000ft and 500ft respectively. Each mote is powered by an external battery. Power management in wireless sensor networks is an important aspect that will drive research in the next decade, and low-power sensor and RF transceivers are crucial for key applications in this area. The stateof-the-art in power management involves defining wake and sleep cycles. A mote draws ma current levels in data-gathering or wake mode and only draws μa current levels in sleep mode. Various other practical aspects including feasibility studies are being pursued. Wireless Sensor Networks Before looking at how wireless sensor networks can be used to assist firefighters in the performance of their duties, it is first necessary to know something about wireless sensor networks in terms of how they work; their capabilities and limitations. A Wireless Sensor Network (WSN) is a network comprised of numerous small independent sensor nodes or motes. They merge a broad range of information technology; hardware, software, networking, and programming methodologies. Wireless Sensor Networks can be applied to a range of applications [1] monitoring of space which includes environmental and habitat monitoring, indoor climate control, surveillance etc.; monitoring things for example structural monitoring, condition-based equipment maintenance etc.; and monitoring the interactions of things with each other and the surrounding space e.g., emergency response, disaster management, healthcare etc. The majority of these applications may be split into two classifications: data collection and event detection. Classification of Possible Application Examples As the technology gains popularity, research is becoming important in both theoretical and application domains. We identify two classes of application examples below. A. Stationary Network: We define a stationary network as a network of sensor nodes, in which, each sensor node s position is fixed relative to the base station and other nodes in the network. A demonstrated application in this direction is humidity monitoring in a vinyard [5]. Data acquired by a mote is transmitted to the base station which then processes the information and triggers necessary actions such as localized watering. There are two possible cases to transmit data. When a node is in direct wireless contact i.e. in the range of the base station, direct communication is possible. When a node is not in the range, it transmits data in an ad-hoc environment also referred to as multi-hop. Implementation of an efficient multi-hop system requires optimal routing [16] to facilitate shortest route, reduced power consumption and improved transmission. B. Network in Motion: An example in this category is a herd of animals on an extensive farm, where each animal is equipped with a sensor node. The animals are in constant motion relative to each other as well as the base station. Such a complicated mobility management requires an even more sophisticated implementation of routing algorithms. In order to maximally benefit from wireless sensor networks of

5 this type, we foresee additional hardware requirements in the form of GPS devices and other forms of mote location. Figure 1: Example of a Flat Network (adapted from [3]) Data Collection versus Event Detection As stated above, in general wireless sensor networks can be categorized into one of two types, data collection or event detection networks. In many applications where data collection is the goal, the sensors may be required to collect data for short periods at set times of the day. In this case, most of the time the sensor node will be asleep thus conserving power. However, where a wireless sensor network is to be employed for event detection, such as detecting the ignition of a fire, it would be anticipated that the sensor nodes must remain awake thus consuming their precious limited power.

6 Figure.2 Currently one of the most popular research platforms is the Mica2 sensor mote shown in Figure 2. It uses the TinyOS (TOS) Distributed Software Operating System, has a 325, 433 or 868/916 MHz multichannel radio transceiver and an expansion connector that can be used for light, temperature, RH, barometric pressure, acceleration/seismic, acoustic and magnetic sensor boards [5]. Figure 3 shows the standard indoor injection molded housings available for the Mica2. Current Research into Wireless Sensor Networks and Firefighting In the past, research into using wireless sensor networks for fire detection has predominantly focused on the detection and tracking of wildfires. It is only in the last few years, since the attack on the World Trade Center, there has been increasing focus on the safety of and communication between firefighters on the fire ground. This has resulted in researchers looking at the possibilities of using wireless sensor networks when fighting structural fires. One group at UC Berkeley has been working on a project called FIRE (Fire Information and Rescue Equipment). The objective of this project is to develop both hardware and software tools to improve the safety, efficiency and effectiveness of firefighting.the research is comprised of three sections, a wireless sensor network called SmokeNet, which forms the basis of the whole project, a head mounted display unit for individual firefighters called FireEye, and an incident command system called eics which will be a visual display showing information such as resource allocation, location of personnel on the floor plans of the building, and biometric data of firefighters including air supply and heart rate. The SmokeNet implementation is based on TinyOS and utilizes Crossbow wireless smoke and temperature detecting sensor nodes. In a non-alert state, these nodes check the status of the environment every 10 seconds and send data obtained along with their battery condition via a multi-hop network to a central building node logger computer every 5 minutes. On detecting a fire, a node sends an alert message which places the whole network in an alert state. In this state, each node checks for fire every five seconds and reports to the data logger every two minutes if no fire is detected to confirm that it is still alive. The system also allows for the Incident Commander to connect into the wireless sensor network, via eics, to determine the location of the fire and also track firefighters and their health status. It also allows for data to be sent to firefighters wearing the FireEye.

7 Another group has been working on a project called Siren Context-aware Computing for Firefighting. The system that they are developing supports tacit communication among firefighters using a contextaware messaging application. Each firefighter carries a WiFi enabled PDA with a built in Berkeley motes sensor board. The mote in the PDA collects data from motes which are pre-deployed in the building to inform the firefighter of hazards and immediate danger. In addition, the pre-deployed motes also serve as location beacons, thus enabling a firefighter to navigate his/her way through the building. Each PDA connects to the PDA s of other firefighters in a peering mode. Application Example in Progress: Wireless Sensor Systems for Patient Monitoring As part of the on-going work, a comprehensive design framework of an intelligent wireless patientmonitoring system has been developed. This framework includes real-time sensing of patient s vital parameters using the motes, and wireless transmission of such critical information over radio frequencies to the base-station. Subsequent data processing on a PC will allow detection of certain medical emergencies, and automatic alerting of medical staff. A simplified prototype system utilizing a basic wireless network kit and sensor components has been implemented for the purpose of experimentation. In phase II of the project, biomedical oriented lowpower high-precision sensors will be designed and possibilities of developing a digital (Bluetooth) version of the wireless senor system will be studied for advancement of the prototype. Prototype system will be tested in a hospital environment and will be refined towards meeting ethical, medical and technological standards in phase III. Potential deliverables will include: An Ad Hoc self-organizing wireless system capable of close patient-monitoring even when patients are in motion. A Smart-Bed with increased automation in terms of emergency detection. These capabilities could lead to efficient use of our valuable healthcare resources. Sensor Network Challenges Support for very large numbers of unattended autonomous nodes, adaptability to environment and task dynamics in are the fundamental challenges of WSNs as they have limitations of dynamic network topology, limited battery power, and constrained wireless bandwidth. The configuration of sensor nodes would frequently change in terms of position, reachability, power availability, and even task details. Because these sensor nodes interact with the physical environment, they would experience a significant range of task dynamics. Node mobility, node failures, and environmental obstructions cause a high degree of dynamics in WSN. This includes frequent network topology changes and network partitions. The partitioned subnetworks need to continue running independently, and the management protocol must be robust enough to adapt this condition. Sensors are energy constrained and subject to unfriendly environments; they can store or reproduce very limited energy from the environment. That is why they fail due to depleted batteries or due to

8 environmental influences. Restricted size and energy typically means restricted resources (CPU performance, memory, wireless communication bandwidth and range). Thus, we need to ensure that network protocol overhead is kept to a minimum so that energy is conserved. The number of packets transmitted/received/processed at each node should be reduced since energy is consumed in these operations. Other issue is that the transmission distance of microsensor nodes can be very short in compare to the conventional macrosensors and handheld devices. So, the transmitted power is low, and hence requires significantly different architectures for intelligent resource efficiency. While some applications such as image sensors demand a high transmission data rate, most sensing applications will require very low data rates compared to conventional multimedia traffic. Existing radio architectures are not suitable for these very low data rates since they have significant energy overhead in powering on and off. WSNs will exist with the plenty of nodes per user (or more). At such heavy quantity, it is impossible to pay attention to any individual node. Furthermore, even if it is possible to consider each node, sensors may be inaccessible, since they are incorporated in physical structures, or thrown into hostile terrain. Thus, for such a system to be effective, it must provide unattended operation and self-configuration functionality. There are many large scale unattended systems exist now a days. For example, automated process and pharmaceutical companies may contain hundreds of largely unsupervised computers as part of SCADA. It can still monitor the different process variables according to the system design. In our case, here, WSN is even bigger and wireless, so we require more consideration. WSN middleware should support the implementation and basic operation of WSN as outlined above. However, this is a not a trivial task, as WSNs have some unique properties different from ad hoc networks. To outline this point, the differences between sensor networks and ad hoc networks are illustrated below: 1. Sensor nodes are densely deployed in compare to the ad hoc network nodes. 2. The WSN has larger number of sensor nodes than a hoc network. 3. WSN network topology changes more frequently than ad hoc network. 4. WSN nodes are inclined to fail more than ad hoc network nodes. 5. WSN nodes are scarce in resources such as limited in power, and memory. 3. WSN Applications The features described above provide a wide range of applications for WSNs. WSNs may consist of numerous diverse kinds of sensor nodes to sense different types of parameters such as acoustic, thermal, visual, magnetic, infrared, seismic, radar, etc. These sensor nodes are able to monitor a wide variety of ambient conditions that include the following: flow, temperature, pressure, humidity, moisture, noise levels, mechanical stress, speed, etc. Smart sensors that can monitor many physical variables can be used with WSN. Many new applications are being developed because of this new concept of microsensing and wireless networking of these smart sensors. Some of the potential diverse applications of WSNs are as follows: temperature control, inventory management, physiological monitoring, habitat monitoring, precision agriculture, forest fire detection, nuclear, chemical, and biological attack detection, military, transportation, disaster relief, and environmental monitoring (air,

9 water, and soil chemistry). WSNs can reform information gathering in a variety of situations. Some of the applications are discussed below in detail. Temperature control: One central controller controls the air conditioning or heating systems of the majority buildings centrally. One central control is not enough to control the airflow evenly in huge room; coldness or heat is not evenly distributed. The airflow and temperature can be controlled by incorporating the WSN in the huge room, so proper turning on and off air conditioning or heating unit can save energy. Inventory management: Sensor node can be attached to every item of inventory, walls, gates or ceiling in a warehouse. These sensor nodes can track the location of the item of inventory all the time. The supervisor or warehouse engineer can find out the location of the item whenever he/she wishes to do so, therefore items can be located by the WSNs automatically and can be reported to the end users. Any unpredicted large-scale movements of items or significant changes in inventory levels are alarmed to the manager or supervisor of the warehouse. In this way, WSNs based systems will remove manual scanning and offer more efficient way of locating the items without spending much money on manpower. Inventory can be updated automatically by binding the sensors to the new arrived items in the warehouse. Physiological monitoring: WSNs can be used to collect and store the physiological variable such as blood pressure, heart rate, etc. of the patients for a long period of time. Doctors can monitor these data remotely for medical examination through the use of WSNs. This is actually more convenient for the patient as they facilitate a higher quality of life compared to the medical hospitals. It also lets the medical examiner to better predict and understand the patient s situation by identifying pre-defined symptoms earlier. Even in the hospital, WSN can be used very efficiently. Tiny sensor nodes of WSN can be attached to each patient to do their assigned tasks such as measuring heart rate, blood pressure and brain activities and so on. Smart sensor can be used to measure these parameters at different times. Doctors can track other doctors by wearing the smart badge on their shoulders, so they can be easily found when they need to find. Habitat monitoring: Habitat monitoring provides a wide collection of sensing modalities and environmental conditions (Cerpa et al., 2001). Think the aim of supporting data collection and model development of composite ecosystems. Scientists and environmental authorities would like to supervise soil and air chemistry, as well as plant and animal species populations and activities. The primary modalities are video (imaging) and audio (acoustics) to track species or phenomena based on sound, or video information. The sensor nodes for this purpose must be deployable in remote locations that lack the power and the communication facilities, motivating the need for low-energy wireless communication. Along with these advances, the sensor nodes also have the ability to connect with the internet, which allows remote users to monitor and control the environment.

10 Precision agriculture: WSNs can also be used to sense the pesticides level in the water, the level of soil erosion, and the air pollution. Smart sensors can help to identify the type, concentration, and location of pollutants. Basically, WSNs will offer the end users such as farmers and agriculture department a better understanding of the agriculture environment. Forest fire detection: WSNs may be deployed randomly, and densely in a forest, sensor nodes can transmit the source of the fire to the fire rescue team or fire fighting department before the fire is expand over other region. Plenty of sensor nodes can be deployed and networked using today s transmission technologies. They might have power recharge capability, such as solar cells, or vibrating method (Chandrakasan et al., 1999) to recharge the battery to cope with limited battery and unattended operation for very long time. Nuclear, chemical, and biological attack detection: In chemical and biological warfare, WSNs can also be used to detect the foreign chemical agents or biohazard agents in the air and the water. WSN containing thousands of sensor nodes can be deployed in the targeted area and used as a chemical warning system that can be very useful to the end users, which help in investigating this type of casualties easily. Defending team can investigate the detail without exposing to nuclear rays with the help of WSN. Military applications: Military command, control, intelligence, surveillance, and targeting systems can be benefited from WSNs. Because of rapid deployment, self-configuration, self-healing and fault-tolerance characteristics, WSNs are very useful monitor and control for military systems. If some nodes are destroyed by the enemy, it doesn t affect the over all military operation since WSN are consist of many rapidly deployed low cost sensor nodes. Military commanders and leaders can utilize the facility of WSNs to monitor the situation of their troops, the status and the availability of the equipment in battlefield. Sensor attached to every troop, vehicle, and equipment can report the status by their own. This information can be aggregated into the sink nodes or base stations and sent to the command leaders. Vehicle tracking: WSNs can be deployed to track the vehicles within a geographic region. Every vehicle in a large metropolitan area can have one or more attached sensors. The sensors are able to detect their location, vehicle sizes, speeds and densities, road conditions and so on. When vehicles come near to each other, they exchange information summaries. These summaries eventually reach across sections of the cities by the internet or satellite to determine the traffic condition and related information to remote end users for analysis. Drivers can also be warned of dangerous driving conditions for alternate routes, and estimate trip times. Car thieves can be identified and caught with the use of the WSN coupled with internet or satellite within a targeted area and report these threats to police stations. Disaster relief: WSNs can be used to map the disaster area. They can be efficiently used to direct the nearest emergency rescue teams to affected sites if sensors are densely scattered over a targeted area.

11 Even if some of these sensors are destroyed due to their location in the disaster area itself, the remaining sensors coordinate their duties and help rescue team to find safe evacuation paths. In future, we foresee that WSNs would be an integrated everywhere in our lives and intensity of use would be more than the present-day personal computers or mobile phones. Realization of these and other applications require wireless ad hoc networking techniques. Although many protocols and algorithms have been proposed for traditional wireless ad hoc networks, they are not well suited for the unique features and application requirements of WSNs. Communication protocols Physical Layer The physical layer is responsible for frequency selection, carrier frequency generation, signal detection, and modulation and data encryption. Thinking the same way as in conventional radio transmission, the main issue is how to transmit as energy efficiently as possible, taking into account all related costs (overhead, possible retransmissions etc.) considering scattering, shadowing, reflection, diffraction, multipath and fading effects. Very little work has been done regarding protocols well suited to the needs of WSNs at physical layer. (Chien et al., 2001) & (Schurgers et al., 2001) discusses the some energy efficient modulation work with low-power direct-sequence spread-spectrum modem architecture. This low-power architecture can be mapped to an ASIC technology to further improve efficiency. Modulation schemes and strategies to overcome signal propagation effects, and hardware design are main open issues for WSNs design and are unexplored. Data Link Layer The data link layer is responsible for the multiplexing of data streams, data frame detection, and error control. It ensures reliable point-to-point and point-to-multipoint connections in a communication network. (Sankarasubramaniam et al., 2003) looks at the question of choosing packet size with energy efficiency into consideration. Many researchers have targeted at taking into account the degree of redundancy that an aggregated message carries on the link layer. MAC Layer Since thousands of sensor nodes are densely scattered in a sensor field, the MAC (Medium Access Control) scheme must establish communication links for data transfer. It forms the basic infrastructure needed for wireless communication hop by hop and gives the WSNs self-configuration ability. Fairly and efficiently share communication resources between sensor nodes must be the other criteria. In most of the research work, the question is how to ensure that the sensor nodes can sleep as long as possible, not being able to communicate. Most of the proposals show at least some aspects of TDMA. Some of the more relevant papers are the PicoRadio MAC (Zhong et al., 2001), the S-MAC (Ye et al., 2002), and the STEM work (Schurgers et al., 2002). Network Layer Network layer is responsible for routing the packet properly and efficiently. Many active researchers are interested in the research of the network layer beyond the MAC and topology control. Ad hoc network routing can be used but the more strict criteria regarding energy efficiency and scalability require new

12 solutions. The traditional routing problems of unicast, multicast, and anycast routing exist in WSN. The geographic routing and data-centric routing can be considered in WSNs (Shen et al., 2001). Transport Layer Transport layer is responsible for overall end to end reliable data delivery of the transmission. Very little consideration has been given so far to find suitable transport layer solution for WSN. Acknowledgement (TCP) is very expensive since sensor node doesn t have enough memory and power. UDP type scheme is need to communicate between sensors and sink. TCP can be used between sink and end user through internet or satellite. Conclusions Wireless sensor networks (WSNs) are more than ad hoc networks. The rigorous miniaturization, hardware, cost requirements, frequent topology changes and optimize use of power are vital issues and are different from normal ad hoc networks. The flexibility, scalability, fault tolerance, high sensing ability, low-cost and rapid deployment characteristics of WSNs create many new and exciting applications. These WSN applications require a rethinking of the basic paradigms with which communication softwares are designed. As WSNs are still a very new research field, much activity is still on going to solve many open research issues. Many scientists and engineers are currently engaged in developing the technologies needed for different layers of the WSN protocol stack. As some of the underlying hardware problems, especially with respect to the energy constraint and miniaturization, are not yet completely solved. These problems could be resolved in the future, but will take very long time to solve them.