DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks

Abstract

Since sensor nodes usually have a large duty cycle interval to prolong network lifetime, duty-cycled wireless sensor networks (WSNs) can suffer from a long end-to-end (E2E) delay. Because delay-sensitive applications have a certain E2E delay requirement, a lot of studies have tried to tackle the long E2E delay problem. However, most existing studies focused on simply reducing the E2E delay rather than considering the delay bound requirement, which makes it hard to achieve balanced performance between E2E delay and energy efficiency. Although a few studies took into consideration both the delay bound requirement and energy consumption, they required specific node deployment or strict time synchronization between nodes in the network. In order to address the limitations of the existing studies, we propose a delay-constrained duty-cycle scheduling (DDS) algorithm. The objective of DDS is to achieve low energy consumption while satisfying the delay bound requirement in various node deployment scenarios depending on user demands. First, based on network topology information collected by the sink, one-hop delay distribution is derived as a function of the duty cycle interval. Then, the E2E delay distribution is estimated using the Lyapunov central limit theorem, which allows each node group to have a different delay distribution. Finally, the duty cycle interval is determined using the estimated E2E delay distribution such that energy consumption is minimized while meeting the delay bound requirement. Practical WSN deployment scenarios are considered to evaluate the proposed algorithm. The simulation results show that DDS can guarantee the given delay bound requirement and outperform existing algorithms in terms of energy efficiency.

A Novel Duty Cycle Based Cross Layer Model for Energy Efficient Routing in IWSN Based IoT Application

“…Dao et al [20] introduced a scheme called Deadline Aware Scheduling and Forwarding (DASF) scheme and Vu et al [21] proposed a scheduling scheme called Delay constraint Duty-cycled Scheduling (DDS), both aim to reduce end-to-end delay and energy consumption in a duty-cycled WSN. These two methods, i.e., DASF and DDS, use the central limit theorem for determining the delay distributions of each group of nodes.…”

“…The performance metrics as probability density function, state estimation error, energy consumption, goodput, duty cycle, sleep scheduling, number of alive nodes, throughput and delay are analyzed for the proposed method and are validated with other similar state-of-the-art techniques which inhibits the approaches like clustering, duty cycle scheduling, and cross-layer optimization i.e., LEACH [16], DEEC [10], SEP [11], Greedy routing [15], CL-CFMPR [3], FMRP [12], DASF [20], CDSWS [13], DDS [21], GCKN [14], X-MAC [6], PB-MAC [7], and RMS [22]. The probability density function for the RCKF method is shown in Fig.…”

QoS Aware Routing Protocol Through Cross-layer Approach in Asynchronous Duty-Cycled WSNs

“…We assume a network with N nodes, where all of them operate under the duty-cycled asynchronous Low Power Listening (LPL) modes [29]. Each sensor only has two possible working states: the active state, in which the sensor can perform all the functions of sensing, listening, transmitting, and receiving; and the dormant state, in which the sensor turns off all the functional modules except for a wake-up timer.…”