Real-Time Data Acquisition in Wireless Sensor Networks [chapter]

Mujdat Soyturk, Halil Cicibas, Omer Unal
2010 Data Acquisition  
Introduction Implementation of low cost sensor nodes in recent years allow sensor nodes to be applicable in many different areas e.g. environment monitoring, homeland security and disaster relief operations. One major contribution is their high demand on data acquisition. Real-time data acquisition is more challenging and promising issue in these application areas. There are many approaches and solutions proposed for real-time data acquisition in the literature. In this chapter, we focus on
more » ... -time data acquisition in wireless sensor networks. Wireless Sensor Networks (WSNs) consist of many tiny wireless sensors which operate in an environment in order to collect data for a specific mission. In most type of WSNs, once sensor nodes are deployed, thereafter no additional actions are employed. In a typical WSN, data is gathered from the environment by sensor nodes, aggregated in intermediate nodes and then transmitted to a base station. Because all these operations are executed by sensor nodes with limited power in a wireless media; reliable communication, power efficiency and network survivability issues are among critical concerns. WSNs are different from traditional networks because of their inherent characteristics. The specific properties of these networks pose various challenges such as energy consumption, limited bandwidth, and low storage. In the following sections we will introduce these constraints in detail. WSNs can be used in a wide range of application areas. Networks e.g. composed of video and audio sensors can be used to provide monitoring and surveillance systems or can be used to enhance the existing ones. Some critical areas for homeland security, such as borders, gulfs, strait entrances and port approach waters, are subject to enemy infiltration in crisis and in wartime. Using an instantly deployable network composed of sensor nodes in these operation areas would be a good solution to increase the probability of detecting a penetration in a cost effective and efficient way than the conventional ones. Some applications, e.g. military operations, introduce additional requirements on sensor and ad-hoc networks such as reliability and operating in real-time. Limited battery life of the nodes requires efficient energy consumption techniques which challenge real-time and reliability requirements. There are many routing approaches to provide either or both of the objectives of reducing the end-to-end delay and providing the reliability. However, most of these routing approaches challenge with other aspects such as energy-efficiency, long-lifetime and low-cost expect of the system. Energy aware protocols in the literature generally use multi-hop paths to use energy more efficiently. However, increase in number of hops between the source and the destination www.intechopen.com Data Acquisition 64 nodes bears some issues that must be considered (Monaco et al, 2006 ) (Du et al, 2006 . First of all, nodes close to the sink deplete their energies quickly; leaving the sink unreachable and forcing the system into off-state . Secondly, increase in the hop-number cause more nodes to buffer the packet on-the-route, causing a processing overhead and delay at inbetween nodes. Processing overhead and buffer fill-up may cause packets to be dropped. On the other hand, delay at nodes may prevent to fulfill the real-time requirements of the system (Monaco et al, 2006) . As the network size grows, the length of the constructed paths will increase, causing the problem described above more challenging. New routing techniques which provide reliability and real-time response to sensor readings in energy efficient way are always required in Wireless Sensor and Ad Hoc Networks. Mobility is the other major concern that hardens the problem. Mobility of nodes degrades the performance of the system, making the problem more challenging and impractical. Mobility introduces additional overhead, increases complexity and makes the conventional routing algorithms fail. Therefore, novel and special algorithms are required for mobile environments. In this study we introduce and discuss some proposed MAC protocols, routing protocols and aggregation techniques which address real-time needs in literature. Then we present two applications for real-time data acquisition using WSN. The structure of this chapter is as follows. In Section 2, we define real time data acquisition and relevant constraints. Real time communication issues are also discussed in this section. MAC layer and network layer protocols are presented in Section 3 and Section 4, respectively. In Section 5, aggregation techniques are stated. We give two sample Real-Time WSN applications in Section 6. Real-time data acquisition Real-time data acquisition can be stated as collecting, processing and transmitting data in predetermined latency boundaries. It mainly includes sampling, MAC layer operations, network layer routing, data aggregation and some additional processes. Real-time data acquisition is a mandatory issue which must be considered in some WSN applications. This application may be a surveillance system, a temperature detector )(Lu et al, 2005 , fire monitoring or intruder tracking system. Thus, the sensor data will be valid only within limited time duration (Felemban et al, 2005) . Real-time QoS is classified into two categories: hard real-time and soft real-time. In hard real-time, end-to-end delay boundaries are described as deterministic values. Latency in a message's delivery higher than this value will be a failure. However in soft real-time, a probabilistic latency value is used and some delay delay is tolerable (Li et al, 2007) . The delay metric in every process stage determines the latency issues in algorithms and approaches. So in order to design a real-time WSN system, each process stage should be well designed. Real time data acquisition constraints in WSN In [Akyildiz et al, 2002] , constraints in WSN are classified as sensor node constraints and networking constraints. These constraints also affect the real time data acquisition. In the Section 2.2, we present relations between real time data acquisition and these constraints. Sensor node constraints These constraints are mostly hardware related. The capabilities and constraints of a sensor node's hardware affect the latency. These constraints are listed as follows:
doi:10.5772/10457 fatcat:ociglubdlfabdlme7g6k2fxzle