Components Of A Wireless Sensor Node

Published Date: 02 Nov 2017

CHAPTER 1

A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location. The main characteristics of a WSN include, power consumption constrains for nodes using batteries or energy harvesting, ability to cope with node failures, mobility of nodes, dynamic network topology, communication failures, scalability to large scale of deployment and ease of use. 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.

A wireless sensor network is a collection of nodes organized into a cooperative network. Each node consists of processing capability (one or more microcontrollers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single omni-directional antenna), have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. Systems of 1000s or even 10,000 nodes are anticipated. Such systems can revolutionize the way we live and work.

Currently, wireless sensor networks are beginning to be deployed at an accelerated pace. It is not unreasonable to expect that in 10-15 years that the world will be covered with wireless sensor networks with access to them via the Internet. This can be considered as the Internet becoming a physical network. This new technology is exciting with unlimited potential for numerous application areas including environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces. Figure 1.1 below neatly depicts the components of a wireless sensor node with five major units, processing unit, sensing unit, transceiver unit, power unit and additional unit respectively.

Figure 1.1 Components of a Wireless Sensor Node

1.2 WHY WIRELESS?

There are situations when it is desirable to make measurements in locations where the use of cabled sensors is problematic. Protecting cables by running them through conduit or burying them in trenches is time consuming, labor intensive, and sometimes not even possible. Local fire codes may preclude the use of certain types of sensor cable inside buildings. In some applications measurements need to be made at distances where long cables decrease the quality of the measurement or are too expensive. There are also times when it is important to increase the number of measurements being made but the data logger does not have enough available channels left for attaching additional sensor cables. Each of these instances can be resolved with a Campbell Scientific Wireless Sensor Network (WSN). A WSN provides a reliable, low maintenance, low power method for making measurements in applications where cabled sensors are impractical or otherwise undesirable. Figure1 describes the components used in a single sensor node. Figure 1.2 depicts the model of wireless sensor network.

Figure 1.2 Wireless Sensor Network

1.3 STANDARDS USED IN WIRELESS SENSOR NETWORK

Standards are used far less in WSNs than in other computing systems which makes most systems incapable of direct communication between different systems. However predominant standards commonly used in WSN communications include:

Wireless HART

IEEE 1451

ZigBee / 802.15.4

1.4 LOCATION MONITORING SEVICE

Wireless sensor networks have recently received a lot of attention due to a wide range of applications such as object tracking and environmental monitoring. Thus one of the central issues in sensor networks is location tracking, whose goal is to monitor the roaming path of a moving object. In these applications, physical data is continuously collected by the sensor nodes in order to facilitate application specific processing and analysis. While similar to the location-update problem in PCS networks, this problem is more challenging in two senses: (1) there are no central control mechanism and backbone network in such environment, and (2) the wireless communication bandwidth is very limited. With sensor networks, the physical world can interact with the internet more closely. Grouping thousands of sensors together may revolutionize information gathering. For example, a disaster detector may be set up so that temperatures of a forest can be monitored by sensors to prevent small harmless brush fires from becoming monstrous infernos. Similar techniques can be applied to flood and typhoon detection. Another application is environment control; sensors can monitor factors such as temperature and humidity and feed these information back to a central air conditioning and ventilation system. By attaching sensors on vehicles, roads, and traffic lights, traffic information can be fed back to the traffic control center immediately. Location-based services can be combined with sensor networks. A mobile agent can be dispatched following a person to provide on-site services (such applications might be attractive for disability people who have such as hearing or visual problems).Sensors may also be used in combination with GPS to improve positioning accuracy.

Habitat and environmental monitoring represent a class of sensor network applications with enormous potential benefits for scientific communities and society as a whole. Instrumenting natural spaces with numerous networked micro sensors can enable long-term data collection at scales and resolutions that are difficult, if not impossible, to obtain otherwise. The intimate connection with its immediate physical environment allows each sensor to provide localized measurements and detailed information that is hard to obtain through traditional instrumentation. The integration of local processing and storage allows sensor nodes to perform complex filtering and triggering functions, as well as to apply application-specific or sensor-specific data compression algorithms. The ability to communicate not only allows information and control to be communicated across the network of nodes, but nodes to cooperate in performing more complex tasks, like statistical sampling, data aggregation, and system health and status monitoring.

Mobile phone tracking refers to the attaining of the current position of a mobile phone, stationary or moving. Localization may occur either via multilateration of radio signals between (several) radio towers of the network and the phone, or simply via GPS. Mobile positioning, which includes location based service that discloses the actual coordinates of a mobile phone bearer, is a technology used by telecommunication companies to approximate where a mobile phone, and thereby also its user (bearer), temporarily resides.Figure1.3 depicts the location tracking system and Figure1.4 depicts location monitoring system.

Localization-Based Systems can be broadly divided into:

Network-based

Handset-based

SIM-based

Hybrid

Figure 1.3 Location Tracking System

There are two representative types of emerging location based services: location-aware content delivery and location sensitive resource management. The former uses location data to tailor the information delivered to the mobile users in order to increase the quality of service and the degree of personalization. Examples include delivering accurate driving directions, instant coupons to customers nearby or approaching a store, or answering location-based queries for nearest resource information like local restaurants, hospitals, gas stations, or police cars within 5 miles upon a car accident. The latter uses location data combined with route schedules and resource management plans to direct service personnel or transportation systems, optimize personnel utilization, handle emergency requests, and reschedule in response to external conditions like traffic and weather. Examples include systems for fleet management, mobile workforce management, and transportation management. Scalable location query processing is an enabling technology for all these applications.

An important research challenge for location information management and future mobile computing applications is a scalable architecture that is capable of handling large and rapidly growing number of mobile objects and processing\ complex queries over mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial and temporal continuous queries on mobile objects in a centralized location monitoring system.

Figure 1.4 Location Monitoring System

1.5 PRIVACY

Locating or positioning touches upon delicate privacy issues, since it enables someone to check where a person is without the person's consent. Strict ethics and security measures are strongly recommended for services that employ positioning.

Data privacy

Data privacy protections target privacy of data collected by a network and queries posted to a network. There are two types of adversaries threatening the data privacy external adversary and internal adversary. The external adversary only eaves drop communication in a network. This kind of adversary can be easily defeated by encryption techniques such as SPINS or pecks On the other hand; the internal adversary controls one or more nodes and usually has an access to encryption keys of these nodes. In such a case, the easiest way to protect privacy of data sent from nodes to the base station is to use end-to-end encryption based on keys shared between the sending node and the base station. However, such encryption makes data aggregation within the network impossible. Therefore, one of the challenges is to provide secure and privacy preserving data aggregation in the presence of an internal adversary. Multiple schemes were proposed to solve this problem. Query privacy in a similar setting was also investigated in since our future research includes the data-oriented privacy only partially, we do not explore this idea further. Context privacy Even though data privacy might be society protected, a sensor network may still leak valuable context-oriented information. Typical context-oriented information is information on source location, sink location and timing of events. This kind of information can be usually obtained by an external adversary using trace analysis techniques [We summarize state-of-the-art protections in the following subsections.

Location privacy

Location privacy is extremely important in WSNs. Information on location of events or on location of base stations can be of a primary concern of an adversary. Suppose the Panda-Hunter Game where a WSN is employed to monitor endangered pandas in their habitat. It is sufficient for the adversary to out location of sensors currently monitoring the panda to successfully localize and capture the panda. Similarly, the adversary only needs to out location of the base station to be able to mount a physical or other DoS attack on the base station and thus inactivate the whole network. There are two basic types of adversaries considered when evaluating the location privacy local adversary and global adversary. The local adversary has limited radio range and is able to monitor only in a small part of the network at a time. On the contrary, the global adversary is capable of monitoring the whole network at a time and is able to immediately localize all transmitting nodes.

1.6 ISSUES REMAIN TO BE RESOLVED FOR THE SUCCESS OF SENSOR

NETWORKS

Scalability: Since a sensor network typically comprises a large number of nodes, how to manage these resources and information is not an easy job. Distributed and localized algorithms are essential in such environments. Also, scalability is a critical issue in handling the related communication problems. The coverage and exposure of an irregular sensor network are formulated as computational geometry problems. This coverage problem is related to the Art Gallery Problem and can be solved optimally in a 2D plane, but is shown to be NP-hard in the 3D case.

Stability: Since sensors are likely to be installed in outdoor or even hostile environments, it is reasonable to assume that device failures would be regular and common events. Protocols should be stable and fault-tolerant.

Power-saving: Since no plug-in power is available, sensor devices will be operated by battery powers. Energy conservation should be kept in mind in all cases. Energy consumption of communications might be a major factor. Techniques such as data fusion may be necessary, but the timeliness of data should be considered too. Data dissemination is investigated in. Mobile agent-based solutions are sometimes more power-efficient.

1.7 APPLICATIONS OF WIRELESS SENSOR NETWORK

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. A military example is the use of sensors to detect enemy intrusion; a civilian example is the geo-fencing of gas or oil pipelines. When the sensors detect the event being monitored (heat, pressure), the event is reported to one of the base stations, which then takes appropriate action (e.g., send a message on the internet or to a satellite). Similarly, wireless sensor networks can use a range of sensors to detect the presence of vehicles ranging from motorcycles to train cars.

Environmental/Earth monitoring

The term Environmental Sensor Networks, has evolved to cover many applications of WSNs to earth science research. This includes sensing volcanoes, oceans, glaciers forests, etc. Some of the major areas are listed below.

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.

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.

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. And through the data gathered it may be possible to know the occurrence of landslides long before it actually happens.

Industrial monitoring

Machine health monitoring

Wireless sensor networks have been developed for machinery condition-based maintenance (CBM) as they offer significant cost savings and enable new functionalities. In wired systems, the installation of enough sensors is often limited by the cost of wiring. Previously inaccessible locations, rotating machinery, hazardous or restricted areas, and mobile assets can now be reached with wireless sensors.

Data Logging

Wireless sensor networks are also used for the collection of data for monitoring of environmental information, this can be as simple as the monitoring of the temperature in a fridge to the level of water in overflow tanks in nuclear power plants. The statistical information can then be used to show how systems have been working. The advantage of WSNs over conventional loggers is the "live" data feed that is possible.

Industrial sense and control applications

In recent research a vast number of wireless sensor network communication protocols have been developed. While previous research was primarily focused on power awareness, more recent research have begun to consider a wider range of aspects, such as wireless link reliability, real-time capabilities, or quality-of-service. These new aspects are considered as an enabler for future applications in industrial and related wireless sense and control applications, and partially replacing or enhancing conventional wire-based networks by WSN techniques.

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 and also used in pollution control board.

Agriculture

Using wireless sensor networks within the agricultural industry is increasingly common; using a wireless network frees the farmer from the maintenance of wiring in a difficult environment. Gravity feed water systems can be monitored 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.

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.

Structural monitoring

Wireless sensors can be used to monitor the movement within buildings and infrastructure such as bridges, flyovers, embankments, tunnels etc... enabling Engineering practices to monitor assets remotely without the need for costly site visits, as well as having the advantage of daily data, whereas traditionally this data was collected weekly or monthly, using physical site visits, involving either road or rail closure in some cases. It is also far more accurate than any visual inspection that would be carried out.

Passive localization and tracking

The application of WSN to the passive localization and tracking of non-cooperative targets (i.e., people not wearing any tag) has been proposed by exploiting the pervasive and low-cost nature of such technology and the properties of the wireless links which are established in a meshed WSN infrastructure.

ABOUT THE PROJECT

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. Many cases of these applications rely in the information of personal locations, for example, surveillance and location systems. For the location monitoring system identity and counting sensors are used earlier. But they immediately poses a major privacy breach. Because the adversary can easily infer the identity of the monitored objects based on the mapped monitored area and pinpoint each object’s exact location.

Thus to preserve personal location privacy, we propose two in-network aggregate location anonymization algorithms, namely, resource and quality-aware algorithms. Both algorithms require the sensor nodes to collaborate with each other to blur their sensing areas into cloaked areas, such that each cloaked area contains at least k persons to constitute a k-anonymous cloaked area. The resource-aware algorithm aims to minimize communication and computational cost, while the quality-aware algorithm aims to minimize the size of the cloaked areas, in order to maximize the accuracy of the aggregate locations reported to the server. Since our system only allows each sensor node to report a k-anonymous aggregate location to the server, the adversary cannot infer an object’s exact location with any fidelity. The larger the anonymity level, k, the more difficult for the adversary to infer the object’s exact location. With the k-anonymized aggregate locations reported from the sensor nodes, the underlying adaptive Spatio-temporal histogram to enable monitoring services without the need of users’ exact locations. The main idea of the histogram is to keep statistics about the distribution of moving objects. At the core of the histogram, we propose three techniques, memorization, locality awareness and packing, to improve monitoring accuracy and efficiency. Furthermore, the histogram is designed in a way that achieves a trade-off between the energy and bandwidth consumption of the sensor network and the accuracy of monitoring services. We evaluate our system through simulated experiments.