Advanced Wireless Networks - 4G Technologies

528 AD HOC NETWORKS

Average delay requirement D (ms)

Figure 13.49 Success ratio [imprecision rate: (a) 5 % and (b) 50 %]. (Reproduced by permission of IEEE [85].)

Comparison of the performance results shown in Figures 13.49–13.52 demonstrates advantages of the TBP. The protocol presented in this section is a brief interpretation of Ticket Based Protocol represented in Chen and Nahrstedt [85] with slightly modified terminology adjusted to that of the rest of the book. The problem of QoS routing in wireline and wireless networks has been attracting much attention in both academia and industry. For more information see References [86–88]. A comprehensive overview of the literature is given in Chen and Nahrstedt [88]. The problem will be revisited again in Chapter 21 in more detail.

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references therein) are outlined below [1–5]:

sensor nodes are densely deployed;

sensor nodes are prone to failure;

the number of sensor nodes in a sensor network can be several orders of magnitude higher than the nodes in an ad hoc network;

the topology of a sensor network changes very frequently;

sensor nodes mainly use broadcast communication paradigms whereas most ad hoc networks are based on point-to-point communications;

sensor nodes are limited in power, computational capacities and memory;

sensor nodes may not have global identification (ID) because of the large amount of overhead and large number of sensors.

One of the most important constraints on sensor nodes is the low power consumption requirement. Sensor nodes carry limited, quite often irreplaceable, power sources. Therefore, while traditional networks aim to achieve high QoS provisions, sensor network protocols must focus primarily on power conservation. They must have inbuilt trade-off mechanisms that give the end user the option of prolonging network lifetime at the cost of lower throughput or higher transmission delay. This problem will be the focus of this chapter.

Sensor networks may consist of many different types of sensors such as seismic, low sampling rate magnetic, thermal, visual, infrared, acoustic and radar. These sensors are able to monitor a wide variety of ambient conditions that include the current characteristics such as speed, direction and size of an object, temperature, humidity, vehicular movement, lightning condition, pressure, soil makeup, noise levels, the presence or absence of certain kinds of objects, mechanical stress levels on attached objects etc.

Sensor nodes can be used for continuous sensing, event detection, event ID, location sensing, and local control of actuators. The concept of micro-sensing and wireless connection of these nodes promises many new application areas. Usually these applications are categorized into military, environment, health, home and other commercial areas. It is possible to expand this classification with more categories such as space exploration, chemical processing and disaster relief.

Wireless sensor networks can be an integral part of military command, control, communications, computing, intelligence, surveillance, reconnaissance and targeting (C4ISRT) systems. They are used for monitoring friendly forces, equipment and ammunition and battlefield surveillance (Figure 14.1). Sensor networks can be deployed in critical terrains, and some valuable, detailed and timely intelligence about the opposing forces and terrain can be gathered within minutes before the opposing forces can intercept them. Sensor networks can also be incorporated into the guidance systems of intelligent ammunition.

Sensor networks deployed in the friendly region and used as a chemical or biological warning system can provide friendly forces with critical reaction time, which decreases casualty numbers drastically. Environmental applications of sensor networks include tracking the movements of birds, small animals and insects; monitoring environmental conditions that affect crops and livestock; irrigation; macroinstruments for large-scale Earth monitoring and planetary exploration; chemical/biological detection; precision agriculture; biological, Earth and environmental monitoring in marine, soil and atmospheric contexts; forest fire

Figure 14.1 Battlefield surveillance.

detection; meteorological or geophysical research; flood detection; bio-complexity mapping of the environment; and pollution study.

The health applications for sensor networks are providing interfaces for the disabled, integrated patient monitoring, diagnostics, drug administration in hospitals, monitoring the movements and internal processes of insects or other small animals, telemonitoring of human physiological data, and tracking and monitoring doctors and patients inside a hospital. More details on sensor networks applications can be found in References [6–64].

14.2 SENSOR NETWORKS PARAMETERS

A sensor network design is influenced by many parameters, which include fault tolerance, scalability, production costs, operating environment, sensor network topology, hardware constraints, transmission media and power consumption. The failure of sensor nodes should not affect the overall task of the sensor network. This is the reliability or fault tolerance issue. Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failures [25, 49].

The protocols and algorithms may be designed to address the level of fault tolerance required by the sensor networks. If the environment where the sensor nodes are deployed has little interference, then the protocols can be more relaxed. For example, if sensor nodes are being deployed in a house to keep track of humidity and temperature levels, the fault tolerance requirement may be low since this kind of sensor networks is not easily damaged or interfered with by environmental noise. On the other hand, if sensor nodes are being deployed in a battlefield for surveillance and detection, then the fault tolerance has to be high because the sensed data are critical and sensor nodes can be destroyed by hostile actions. As a result, the fault tolerance level depends on the application of the sensor networks, and