# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 5 Discussion
### General Challenges and Open Research Issues

Table 3: Synoptic overview of the algorithm-based methods for energy sustainability. Likewise, due to the handling of multimedia data, Wireless Multimedia Sensor Networks (WMSNs) have special needs in terms of both the nature (i.e., images, video, etc.) and the volume of data and at the same time extremely high requirements regarding QoS metrics that need to be supported [124; 125; 126; 127; 128; 129; 130]. Another issue is that research in many of the abovementioned methodologies is based on tests performed by using computer aided simulations. However, some recent technological advances (e.g., in energy harvesting and energy transfer) are not sufficiently supported by the existing simulation platforms in terms of corresponding energy models of high accuracy [19]. Likewise, there is lack of specialized equipment for wireless energy transfer in corresponding emulation tests performed using testbeds, due to its high cost [19]. Therefore, existing simulation and emulation tools have to be enhanced. Additionally, Artificial Intelligence and Computational Intelligence can provide extremely good support to the development of mechanisms for energy sustainability in WSNs that are able to self-learn and thus easily adapt to any alterations that dynamically take place in the structure or/and operation of WSNs. Specifically, the use of methods, such as Machine Learning, Fuzzy Logic, Neural Networks, Artificial Immune Algorithms, Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, is very promising [131; 132; 133; 134; 135; 136; 137; 138; 139; 140]. Moreover, due to the scientific and technological advancements that are performed, there is a continuous growth of applications that incorporate the usage of Aerial Wireless Sensor Networks (AWSNs), Wireless Underground Sensor Networks (WUSNs) and Underwater Wireless Sensor Networks (UWSNs). However, the majority of the aforementioned methodologies that pursue energy sustainability in WSNs have been designated to be applied in terrestrial WSNs and cannot be used in non-terrestrial WSNs because of the extraordinary conditions that exist in such networks. For this reason, novel research efforts have to support energy sustainability in non-terrestrial WSNs, too [141; 142; 143; 144; 145]. Going one step further, in order to achieve high sustainability along with optimal overall performance in a WSN, metrics other than energy should be considered, too. For this reason, hybrid schemes have to be developed. Specifically, the extension of network connectivity should be preserved because whenever a node loses its connection with its neighboring nodes due to either its malfunction or its energy depletion, there is a certain increase of energy cost for the communications of the remaining active nodes [146; 147]. Furthermore, in order to achieve the optimal utilization of nodes existing in a WSN and thus the optimization of the overall deployment of the network, the maximization of coverage is necessitated [148; 149; 150]. Additionally, congestion avoidance and congestion control [151; 152; 153; 154] mechanisms should be used because congestion obstructs communication, generates packet losses and thus imposes packet retransmissions that exhaust the energy of nodes. Moreover, schemes for the establishment of QoS in terms of general or specific performance metrics are required [155; 156]. Furthermore, security schemes for WSNs to assure confidentiality, authentication, integrity, availability, and freshness are required [157; 158; 159]. However, the achievement of energy efficiency along with other performance metrics is not a trivial task to accomplish. This is because the conditions that must be fulfilled are not only several but they also are opposing in many cases. Likewise, the concurrent deployment of different methods for energy sustainability may also be obstructed due to the opposing requirements that they may have. For this reason, multi-objective optimization algorithms that pursue the concurrent achievement of multiple criteria [160; 161] have to be developed.