# Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
## 1 Introduction  
In recent years, the reduction in the cost of sensor nodes has opened various new areas where Wireless Sensor Networks (WSNs) can be used. Some of the applications of WSNs include environmental, AC/oil/gas/water pipeline, river, border and health monitoring [1]. Underwater sensor networks [2, 3] and software-defined wireless sensor networks [4] are also creating interest among the researchers. The nodes in WSNs have a number of constraints, e.g., limited power capability, limited communication range and small size [1]. Due to these constraints, the algorithms and techniques developed for other systems cannot be directly used without sacrificing the efficiency of the system. In certain applications, a one-dimensional linear wireless sensor network must be established. The example of such applications includes border, oil/gas pipeline and sea/river shore monitoring. For such applications, a new subclass of sensor networks, named Linear Wireless Sensor Networks (LWSNs) has been coined by the researchers [5]. The monitoring of water, gas and oil pipelines using Linear Wireless Sensor Networks has been discussed by several researchers [6; 7; 8; 9; 10; 11; 12; 13]. Water, gas and oil pipelines cross states, countries and sometimes also continents and their length varies between a few kilometers to thousands of kilometers. Modern large smart buildings also use various types of pipelines such as AC, water and Piped Natural Gas (PNG) pipelines, which may cross each other. The length of such pipelines varies from a few hundred meters to a few kilometers. Moreover, the network of pipelines in a smart building is a combination of various pipelines, which cross each other at different points. The number of skyscrapers is bound to increase due to the scarcity of premium space and increasing populations. Moreover, buildings are getting smarter day by day. The reduced cost of sensors has made it possible to extensively use sensors for monitoring and safety purposes in smart buildings. Recently, [PERSON] et al. [14] published a survey to describe the techniques currently being used to improve the performance of sensor-based systems used for applications in intelligent buildings such as improving air quality, lighting, heating/cooling, ventilation, power management, water management, and cooking gas management. In LWSNs, the nodes are placed in a strictly linear or semi-linear form. Due to their special topology and application requirements, solutions used for ordinary sensor networks cannot be directly applied to LWSNs. The LWSNs can be classified either based upon topology (thick/thin/very thin) or based upon hierarchy (one level/two level/three level) [15]. Once an LWSN is deployed, it may not be feasible or cost effective to change the power source in sensor nodes frequently. Thus, while deploying the LWSN one of the most critical and challenging tasks is to deploy sensor nodes effectively so that the deployed network must be long-lived with minimum energy consumption. The major research challenges in LWSNs include node placement, coverage, topology, routing, lifetime optimization, and energy efficiency [5]. Some of the issues are inter-related with each other, for example efficient node placement will increase the coverage and lifetime of the network. Similarly, an efficient routing protocol will save energy and hence the lifetime of the network. Node placement, routing, and lifetime optimization are important research problems in the LWSN field. In [16; 17; 18; 19; 20; 21; 22; 23] various node placement schemes and routing protocols for increasing the lifetime of LWSNs have been proposed. The optimal node placement plays an important role in the energy efficiency and lifetime of a network. Therefore, in recent years several optimization methods have been applied for this purpose. In [24], [PERSON] et al. presented a greedy approach-based technique for optimal sensor placement in an oil pipeline. Their main objective was to maximize the lifetime of the linear sensor network.