Delay QoS and MAC Aware Energy-Efficient Data-Aggregation Routing in Wireless Sensor Networks

By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account

An Efficient Lagrangean Relaxation-based Object Tracking Algorithm in Wireless Sensor Networks

In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time

Enhanced delay-aware and reliable routing protocol for wireless sensor network

Wireless Sensor Networks (WSN) are distributed low-rate data networks, consist of small sensing nodes equipped with memory, processors and short range wireless communication. The performance of WSN is always measured by the Quality of Service (QoS) parameters that are time delay, reliability and throughput. These networks are dynamic in nature and affect the QoS parameters, especially when real time data delivery is needed. Additionally, in achieving end-to-end delay and reliability, link failures are the major causes that have not been given much attention. So, there is a demanding need of an efficient routing protocol to be developed in order to minimize the delay and provide on time delivery of data in real time WSN applications. An efficient Delay-Aware Path Selection Algorithm (DAPSA) is proposed to minimize the access end-to-end delay based on hop count, link quality and residual energy metrics considering the on time packets delivery. Furthermore, an Intelligent Service Classifier Queuing Model (ISCQM) is proposed to distinguish the real time and non-real time traffic by applying service discriminating theory to ensure delivery of real time data with acceptable delay. Moreover, an Efficient Data Delivery and Recovery Scheme (EDDRS) is proposed to achieve improved packet delivery ratio and control link failures in transmission. This will then improve the overall throughput. Based on the above mentioned approaches, an Enhanced Delay-Aware and Reliable Routing Protocol (EDARRP) is developed. Simulation experiments have been performed using NS2 simulator and multiple scenarios are considered in order to examine the performance parameters. The results are compared with the state-of-the-art routing protocols Stateless Protocol for Real-Time Communication (SPEED) and Distributed Adaptive Cooperative Routing Protocol (DACR) and found that on average the proposed protocol has improved the performance in terms of end-to-end delay (30.10%), packet delivery ratio (9.26%) and throughput (5.42%). The proposed EDARRP protocol has improved the performance of WSN

Delay QoS and MAC Aware Energy-Efficient Data-Aggregation Routing in Wireless Sensor Networks

By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account