A wireless sensor network (WSN) differs from the conventional communication networks in terms of architecture and deployment. Dynamic in nature, the WSNs have added dimensions; the further exploration in a hard-to-reach environments and the complexity of network management. WSNs are emerging rapidly and have already demanded keen interest from researchers. As a relatively new concept in the world of communication, a number of aspects are being explored by the researchers, including the routing and security features of WSNs. Like any wireless network, power consumption has always been a critical issue for wireless sensor networks. This can also be due to the environmental constraints in which the wireless sensor networks are normally deployed. This paper provides an overview of this emerging technology by addressing its architecture, deployment and key issues e. g. energy-efficiency, routing, reliability and security
Wireless sensor networks (WSNs) are dynamic, ad hoc wireless networks that have attracted the attention of researchers over the last few years and are part of an emerging technology that is being used to collect information for various environmental phenomena. WSNs comprise a number of wireless sensor nodes (Yick, Mukherjee & Ghosal, 2008; Ganesan et al., 2004). WSNs are being used in a variety of fields and can be used in diverse domains such as the home, office, in automation and control, transportation, logistics, healthcare, environmental monitoring, security and surveillance, asset tracking and monitoring, process monitoring, vehicle monitoring and detection (Ameen & Kwak, 2011; Garg, Saroha & Lochab, 2011). As with any other wireless communication technology, WSNs are concerned with energy-efficiency, security, reliability and scalability (Yick, Mukherjee & Ghosal, 2008; Ganesan et al., 2004; Haider & Yusuf, 2009).
Essentially, a WSN is a networked arrangement of wireless sensor nodes. These nodes can communicate among themselves by means of wireless. The nodes are equipped with different types of sensors depending on the application; hence the name: wireless sensor network. The sensor nodes sense data from the environment by means of sensors. The data is processed and forwarded to another sensor node which, in turn, forwards the information to another node. This process continues until the data eventually reaches the gateway node. The gateway node is a wireless sensor node that interfaces all the other nodes of a WSN to a server computer (Haider & Yusuf, 2009; Singh et al., 2010; Garg, Saroha & Lochab, 2011). The server stores the delivered information for further processing. As all the nodes in a WSN act as routers apart from their respective sensing, processing and transmitting tasks, it is crucial that the sensor nodes of a WSN co-operate and collaborate (Singh et al., 2010). The server can access or control any specific sensor nodes for network management or any other purpose.
The gateway node can have wired or wireless connectivity to the server; this is determined by the environment where the network is deployed. A WSN may be small and consist of just a few sensor nodes or be large, with several thousand sensor nodes. The wireless sensor nodes of a WSN have three major parts – a radio, a microcontroller and a sensor. The radio is for wireless transmission, the microcontroller is the processing unit for the node and the sensor acquires information from the environment. The nodes need to be tiny and inexpensive to ensure the deployment and financial feasibility of the WSNs. A sensor node can be as tiny as a coin and this tiny WSN node has the power to sense, process, route and transmit information (Kalita & Kar, 2009; Garg, Saroha, & Lochab, 2011).
The advantage of WSNs is that they are capable of being deployed in remote environments which humans either cannot access or where they cannot establish a long-term presence for monitoring or research purposes (Khedo, Perseedoss & Mungur, 2010). A WSN can be deployed in deserts, underwater, in a battle field or in any other hard-to-reach environments. WSNs can be used for agricultural purposes. Automatic monitoring can be embedded for precision agriculture; sensors can detect soil moisture, light levels and the temperature from different points of an agricultural field (Shinghal et al. 2011; Wang, Zhang & Wang, 2006) and the data acquired can be analysed. Chemical plants or other industrial plants can be automatically monitored and, if necessary, the alarm can be raised automatically (Garg, Saroha & Lochab, 2011; Kumar, Dhulipala, Prabakaran & Ranjith, 2011; Raazi & Lee, 2010). Underwater deployment of WSNs can help to monitor phenomena related to underwater life and detect unwanted events, e.g. the increase of harmful elements in the water, underwater surveillance, and so on (Pompili, Melodia & Akyildiz, 2006; Ovaliadis, Savage & Kanakaris, 2010). WSNs have already proven their applicability to healthcare where seamless monitoring of the patients has become possible (Ameen & Kwak, 2011; Garg, Saroha & Lochab, 2011; Khan, Hussain & Kwak, 2009; Raazi & Lee, 2010). For smart homes and offices, WSNs have their own appeal. One of the most valuable applications of WSNs is in the field of security and surveillance with the capacity to monitor and collect information. Because any violations can be predicted, monitored and captured, there is the assurance of a safer community (Raazi & Lee, 2010). In the near future, WSNs will contribute to almost every aspect of human life.
The deployment of WSNs is more cost-effective than their counterpart, wired networks. Wired networks require a substantial investment for planning and developing a wired infrastructure and for laying the wires. Drastically reduced installation costs are one of the driving factors behind WSNs becoming popular over the years (Haider & Yusuf, 2009; Wang, Zhang & Wang, 2006). An ideal WSN is smart, software programmable, reliable over the long term, cheap, easy to install, fast in data acquisition and demands almost no real maintenance.
As WSN nodes may be deployed in remote or hard-to-reach and hazardous environments, they need to be left alone for long periods of time with the expectation that they will keep functioning without interruptions caused by power failures. Thus, energy efficiency has always been a critical factor for WSNs (Yick, Mukherjee & Ghosal, 2008; Tummala & McEachen, 2008; Baoqiang, Hongsong & Yongjun, 2008). For practical reasons, the sensor nodes of a WSN tend to be tiny which means that their capacity for storing power is reduced. Though the sensor nodes can be operated by means of solar power, the provision to use this source of power is very limited due to the various environments where the WSNs are deployed. For this reason, the sensor nodes are mainly powered by batteries. The energy from the battery has three purposes. First, energy is needed to keep the sensor node alive. Secondly, battery power is used to process data received or to be sent. The third functionality that consumes energy is the transmission. Batteries are always limited in power and it is this constraint that has always been a challenge for WSNs. Maximising the battery life is needed to make the sensor networks more energy-efficient. As this is cannot always be achieved to the desired level, some different approaches have also been adopted. One approach is to minimise the processing and transmission overheads to a minimum to save battery power (Tummala & McEachen, 2008; Baoqiang, Hongsong & Yongjun, 2008; Correia et al., 2007; Baoqiang & Yongjun, 2007). This has a significant impact on the energy-efficiency of WSNs as power consumption is directly proportional to the amount of processing or transmission. Another approach is known as ‘wake-up-on-demand’. In this, the nodes remain asleep all the time, using minimum power, until they are needed to perform any transmission or processing task (Jones & Atiquzzaman, 2007; Akyildiz, 2002). On demand, the sleeping nodes wake up and perform their task and then go back to standby mode, saving power and making the WSNs energy-efficient. To accomplish the above approaches, a number of different algorithms have been proposed (Heinzelman, 2000; Wang, 2006; Heidemann & Estrin, 2002).
In WSNs, the meaning of routing has acquired a level of dynamism and means a lot more than its conventional meaning of justifying and determining the path through which data should travel from source to destination. A routing decision is taken by the routers of any given network. They need to work in a collaborative and co-operative manner so that data can reach the destination while the optimal and efficient use of network resources is ensured (Singh et al., 2010; Akyildiz, 2002). In the case of WSNs, the concept of routing is somewhat different from conventional networks (Yick, Mukherjee & Ghosal, 2008). Interestingly, all the sensor nodes in a WSN need to perform routing tasks as part of their total functionality. This makes WSNs very complex from a routing point of view. The dynamic nature of WSNs means that any sensor node can ‘die’ at any time, can ‘wake up’ on demand, or the sensor nodes can be moving all the time – the total network scenario constantly changes (Jones & Atiquzzaman, 2007; Akyildiz, 2002). The changing scenario needs to be learned by all the routing elements of a network to maintain the robustness of routing and WSNs are no exception. This dynamism contributes to added complexity in WSN routing algorithms (Ganesan et al., 2004; Heinzelman, 2000). As routing is associated with an enormous amount of information processing which, in turn, has a direct impact on power consumption, an energy-efficient routing algorithm has been of great interest since the of birth of the concept of WSN (Singh et al., 2010). There are a number of proposals on energy-aware MAC protocol and energy efficient routing. Examples of some approaches are physical-level design decisions including voltage and modulation scaling (Kumar et al., 2011).
One of the major considerations for any communication network is the reliability of data transmission. Achieving reliable data transmission in a WSN is difficult. One reason for this is the limited processing capability of the sensor nodes. The limited transmission range of the sensor nodes is another barrier to reliable data transmission. Further, the sensor nodes are deployed close to ground level which leads to signal attenuation. While energy efficiency is one of the goals to be achieved for WSNs, it is a problem for reliable data transmission when the ‘wake-up-on-demand’ approach is adopted (Jones & Atiquzzaman, 2007). All these characteristics may cause data loss within the context of a WSN.
At the same time, WSNs provide some unique features to deal with the issue of reliability. Data aggregation is one of the features by which reliability can be improved. Data aggregation makes the loss of data acceptable up to a certain level. As the sensor nodes are normally deployed in a dense fashion, a number of possible routing paths also improve the reliability. A consequence of data aggregation is the use of smaller data packets which minimise data loss. Though reliability is an issue for WSNs, dense deployment and data aggregation properties make them less tolerant (Singh et al., 2010). Future research in WSNs will essentially involve developing new algorithms to address the reliability problem of the sensor nodes (Kumar et al., 2011). Addressing reliability is very important for WSNs as it has a direct correlation to scalability, power efficiency, mobility and responsiveness (Blacket et al., 2003).
Security is a general concept within the context of communication networks and addresses authentication, integrity and privacy. Security is also a concern for WSNs which are vulnerable to security threats like any other wireless network (Kalita & Kar, 2009). Having the characteristics of unguided transmission and broadcasts, WSNs are prone to eavesdropping where sniffing the transmitted data is possible. The various security issues and threats experienced by wireless networks apply equally to WSNs.
One common security threat for WSNs is the DoS (Denial of Service) attack which arises from malicious acts. The transmitted information in WSNs can be attacked while it is in transit. As WSNs are vulnerable to eavesdropping, the transmitted information can be monitored, interrupted, intercepted or modified (Blacket et al., 2003; Wang & Schulzrinne, 2004). In a Sybil attack, a sensor node forges identities from one or more sensor nodes (Wang & Schulzrinne, 2004). Another type of attack is known as a blackhole attack. This type of attack is associated with a malicious node which acts as a blackhole to attract all the traffic from other sensor nodes (Culpepper, 2004). A critical attack on WSNs is known as a wormhole attack. In a wormhole attack, the attacker intercepts and records transmitted information from one place on the network. The intercepted information is then forwarded into another part of the network. This critical attack is distinguished from other types of attacks in that no compromising of sensor nodes is required for carrying out a wormhole attack (Perrig & Johnson, 2003).
Security models for WSNs have been proposed for different types of threats. Some proposed models show that the weakness of the WSNs can be turned so that they will act as a strength against security threats. A holistic approach has been proposed for the achievement of a more secure WSN. The holistic approach addresses the improvement of the WSN performance in a dynamic environment in terms of security, connectivity and longevity (Avancha, 2005).
WSNs are emerging rapidly due to their diverse applications. In the last few years, notable improvements in and successful applications of WSNs have been observed. Despite having issues related to energy-efficiency, security, reliability and scalability, the ongoing research and development has already made the WSN a promising and evolving technology.
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