# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 5 Discussion
### Challenges and Open Research Issues in Algorithm-Based Methods

Table 2: Synoptic overview of the hardware-based methods for energy sustainability. Compression relies on the assumption that the energy needed to compress the data, is less than that needed to transmit the raw (uncompressed) data while the accuracy of measurements is preserved. Additionally, this method may degrade QoS (i.e., accuracy, latency, fault tolerance and security) while trying to increase the network lifespan [21]. In-Network Processing is associated with non-negligible energy consumption [47]. Similarly, due to the fact that high computational power is required to create a prediction model, Data Prediction is suitable for networks where powerful sensor nodes with high capacity batteries are exploited [23; 54]. Regarding Data Acquisition methods, Adaptive Sampling is characterized by high complexity and high computational overhead, thus necessitating central control [56]. Hierarchical Sampling sacrifices accuracy to achieve energy efficiency, because simple sensors with low accuracy are mostly used while powerful sensors are used only when an event is detected and enhanced data are needed [57]. In Model-based Active Sampling mechanisms complex computations are needed, thus increasing processing load [58]. In Duty Cycling methods, the use of specialized modules is necessary [107; 108; 109]. In duty cycling via Topology Control methods, both location driven and connectivity driven mechanisms take the knowledge of the positions of all network nodes as granted. Yet, this is not a trivial condition to fulfil. Additionally, if GPS modules are used to overcome this problem, such modules not only are costly but also cause interference to network communications [19; 23]. In relation to Sleep/Wakeup mechanisms, On-Demand methods require the existence of an additional radio for wakeup signaling, while in Scheduled Rendezvous clock synchronization problems occur, and in Asynchronous Schemes robustness trades off for energy consumption while latency is high [20; 23]. Regarding MAC protocol mechanisms, scheduled MAC methods are costly to implement. In addition, there are various weaknesses such as the hidden terminal problem (in CSMA) and the clock synchronization problem (in TDMA) [67]. Contention-based MAC protocols, suffer from high latency in packet delivery while in hybrid MAC protocols complexity increases accordingly to the number of network nodes [68]. Thus novel research must be carried out [111; 112]. Finally, as already mentioned, Energy Efficient Protocols may be classified according to Communication Model, Network Structure, Topology, and Reliable Routing [51; 74]. Regarding the first of these categories, Query-based protocols are not suitable for continuous data delivery, while Coherent/Non-Coherent protocols are related with low scalability, high overhead, and high end-to-end delay, and Negotiation-based protocols do not guarantee data delivery [51]. In the Network Structure category, Flat protocols have low scalability while Hierarchical protocols suffer from high overhead and high complexity and do not guarantee optimal routing [51]. In Topology category, Location-based protocols have high overhead and low scalability and require the use of GPS modules that are costly and interfere with network communications [74]. Mobile Agents-based protocols have low scalability, high latency, and high complexity, while Mobile Sink-based protocols suffer from delays on data delivery in routing paths and topology. In Reliable Routing category, both QoS-based protocols, and Multipath-based protocols suffer from high processing load [51]. In order to provide a synoptic overview of the aforementioned statements, the basic operation along with the main advantages and disadvantages of each one of the algorithm-based methods for energy sustainability in WSNs are presented in Table 3.