# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 2 Related Work
proposed the delay-bounded transmission power control (DTPC) algorithm, which aims at minimizing the energy consumption of sensors while meeting the delay constraint [21]. With a stronger transmission power, the E2E delay can be reduced, but energy consumption may increase. Therefore, under DTPC, nodes select an optimal power level for the considered objective. However, they require network-wide time synchronization and wake-up schedule synchronization between neighboring nodes. [PERSON] et al. [25] took into consideration both the delay bound requirement and energy consumption. In this scheme, the E2E delay distribution is estimated using the duty cycle interval and the number of potential forwarders of nodes. It was shown that the delay bound requirement can be satisfied with low energy consumption by adjusting the duty cycle interval. However, this work required specific node deployment, i.e., nodes should be randomly deployed following a uniform distribution in a circular area, where the sink is placed at the center. In addition, it was also assumed that one-hop delays are identically distributed. In contrast, our work does not require a specific node deployment strategy or identically distributed one-hop delays, which makes it much more practical in various sensing scenarios. Table 1 summarizes the differences between our proposed work and existing studies which aimed at improving packet latency. As can be seen in Table 1, most existing studies focused on simply reducing the E2E delay, rather than considering the delay requirement [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. That means that the studies in [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] could not guarantee the latency bound for delay-constrained applications. In addition, there are some studies which have attempted to address the delay constraint problem in duty-cycled WSNs [20; 21; 22; 25]. However, some algorithms [20; 21; 22] required strict time synchronization between nodes which could incur considerably high communication overhead for exchanging synchronization information in the network. Unlike the studies in [20; 21; 22], our proposed DDS algorithm does not require nodes to synchronize their wake-up schedules, leading to low packet overhead. Moreover, even though time synchronization between sensor nodes is not needed, the applicability of the algorithm in [25] is restricted by the assumption that nodes are randomly deployed with a uniform distribution. In many practical scenarios where sensor nodes are placed based on specific application demands, this assumption may not be satisfied. Therefore, we aim at designing a duty-cycle scheduling algorithm which can be applied to WSNs with a custom node deployment. In summary, in order to address the limitations of the existing studies, the delay-constrained duty-cycle scheduling (DDS) algorithm is proposed. Our work focuses on developing a duty-cycle scheduling algorithm which reduces energy consumption in the network while meeting the delay requirement in duty-cycled WSNs. Moreover, there is no time synchronization procedure, and the DDS scheme can be used for various node deployment scenarios.