Energy Aware Routing Strategies for an Evolving Wireless Sensor Network: A Survey

Introduction

Wireless Sensor Networks (WSNs) are constantly revolutionizing the dynamics of the human civilization via its contemporary emerging paradigms such as the Internet of Things (IoT) (Chui, Löffler, & Roberts, 2010) and enabled Wireless Multimedia Sensor Network (Ehsan & Hamdaoui, 2012). Positive market expansions have been displayed by wireless based products and with an indeed accelerated pace. Thus, it seems logical to project that within the next 10 to 15 years the world will be dominated by Internet accessible WSN that are blended into the essence of each human life (Singhal, Gankotiya, Agarwal, & Verma, 2012). The harnessing of cohesive resources to bridge sensor abilities in context of physical innovation is a clear indicator of the potential investment. WSNs are real-time in nature with highly constrained resources, however with enormous potential to be used for numerous and heterogeneous applications. A substantial portion of these constraints are related to the mobile and wireless features of sensor nodes that have a direct relation with the consumption of energy. Therefore, the trade-off of possessing portability in WSN is the loss of a fixed source of energy. Acknowledging energy as being among the critical and core members of the resource repository, it has an absolute influence on the viability WSN. It requires devotion on the signification of the design and development pertaining energy issues in WSNs. Adverse energy dissipation and unbalance energy consumption are among these prevailing issues. The unbalance energy consumption problem is caused by the physical and logical attributes of a node (i.e. location), which imposes differing demands. These distinctions categorize certain nodes as critical nodes exemplifying their responsibilities especially within the multihop communications architecture. The gradient of nodes subjects to its selective location within a super group of divisional zones of Base Station (BS) creates an uneven energy consumption topology. This is mainly due to the differences in terms of location which imposes a differentiated workload. The nodes which are located geographically closest to the BS are responsible to deliver its own data in addition to those belonging to other nodes. These nodes are faced with the constant threat of being subject to consume large portions of energy as compared to other nodes. These critical nodes eventually emerge most unwillingly among the set of nodes to be the node that will die first. These network divisions of nodes are also referred as the hotspot area. The subsequent problems which rise from this, are the uneven network and relative energy consumption. To ensure that these energy issues does not pose as a hindrance factor to further portability, serious attention must be placed on energy management issues such the energy dissipation and unbalanced consumption.

The wide spectrum and state-of-the-art research (Jiang, Shi, Xiang, & Tang, 2010; Liu & Lin, 2003; Soro & Heinzelman, 2005) in this area serves a testimony for the constant effort to address challenges which energy consumption causes. These constitutes substantial efforts to address the challenge discussed above done within the parameters of research, industry and the consorted of government agencies (Asín & Gascón, 2012; Wendi B. Heinzelman, Murphy, Carvalho, & Perillo, 2004; Shen, Chavalit Srisathapornphat, & Jaikaeo, 2001). Innovative solutions in the design of energy sensitive nodes have been done by ANT and ZigBee (Mainetti, Patrono, & Vilei2, 2011) (An uncommon abbreviation used for ZigBee is ZB). In parallel to physical milestones being achieved, algorithmic solutions have been pursued to an extreme extent. This chapter reviews these substantial trend-setters and the path paved for the elevation of WSN. In this chapter, a distinction is formed in two folds. First, a detail review and analysis is done on each of the identified governing paradigms. Second, each algorithm is dissected in detail and deliberation on their comparative performance analysis has been done. Thus, the correlations between the algorithms are vividly displayed at the completion of the chapter. The chapter is presented in the following order. In section II the transcending paradigms of WSN is presented. Section III reviews diverse survey chapters under a comparison of existing survey on WSN. Section IV provides emerging routing algorithms for conventional WSN, Wireless Multimedia Sensor Network (WMSN) and Wireless Sensor Network-Internet Protocol (WSN-IP) as well. Conclusion and future work are proposed in the last section.