A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks

Abstract

Many applications in wireless sensor networks (WSNs) require energy consumption to be minimized and the data delivered to the sink within a specific delay. A usual solution for reducing energy consumption is duty cycling, in which nodes periodically switch between sleep and active states. By increasing the duty cycle interval, consumed energy can be reduced more. However, a large duty cycle interval causes a long end-to-end (E2E) packet delay. As a result, the requirement of a specific delay bound for packet delivery may not be satisfied. In this paper, we aim at maximizing the duty cycle while still guaranteeing that the packets arrive at the sink with the required probability, i.e., the required delay-constrained success ratio (DCSR) is achieved. In order to meet this objective, we propose a novel scheduling and forwarding scheme, namely the deadline-aware scheduling and forwarding (DASF) algorithm. In DASF, the E2E delay distribution with the given network model and parameters is estimated in order to determine the maximum duty cycle interval, with which the required DCSR is satisfied. Each node independently selects a wake-up time using the selected interval, and packets are forwarded to a node in the potential forwarding set, which is determined based on the distance between nodes and the sink. DASF does not require time synchronization between nodes, and a node does not need to maintain neighboring node information in advance. Simulation results show that the proposed scheme can satisfy a required delay-constrained success ratio and outperforms existing algorithms in terms of E2E delay and DCSR.

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

“…We also note that the proposed algorithm can overcome the limitations of our previous work [25], which assumed that nodes are randomly deployed following a uniform distribution in a circular area. By estimating the delay distribution based on the collected network topology information, the algorithm in this work is suitable for general cases where node deployment scenarios depend on various user demands.…”

“…By estimating the delay distribution based on the collected network topology information, the algorithm in this work is suitable for general cases where node deployment scenarios depend on various user demands. Moreover, existing studies [25,26] had to assume that one-hop delays of different node groups are identically distributed in order to use the classical central limit theorem. In contrast, this work allows each node group to have a different delay distribution by proving the Lyapunov condition and using the Lyapunov central limit theorem [24], which results in a higher performance in practical WSN scenarios.…”

“…In order to validate the performance of DDS, we compare DDS with four algorithms: the optimal anycast packet-forwarding (OAPF) [16], the optimal geographical progress (OGP) [19], and two extended deadline-aware scheduling and forwarding (DASF) [25] algorithms. The proposed algorithm was evaluated using performance metrics, including the achieved DSR, the total energy consumption by nodes in the network, and the average E2E delay.…”

“…Unlike several existing works [7][8][9][10][11][12][20][21][22] which needed strict time synchronization, DDS does not require time synchronization nor wake-up synchronization. Moreover, in contrast to some studies which assumed a specific node deployment [18,19,25] (i.e., uniformly random distribution), the proposed duty-cycle scheduling algorithm can be applied to WSNs with various node deployment scenarios.…”

Adaptive Sensor Node Sleep Scheduling for Quality-of-Experience Enhancement

“…For example, Shi et al [15] and Dao et al [16] have proposed adaptive sleep schedule techniques that aim towards satisfying the end-to-end delay requirement of applications. However, we propose an adaptive sleep mechanism that aims at maximizing energy efficiency while providing assured quality.…”