Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks

Abstract

The quality of service (QoS) regarding delay, lifetime and reliability is the key to the application of wireless sensor networks (WSNs). Data aggregation is a method to effectively reduce the data transmission volume and improve the lifetime of a network. In the previous study, a common strategy required that data wait in the queue. When the length of the queue is greater than or equal to the predetermined aggregation threshold (Nt) or the waiting time is equal to the aggregation timer (Tt), data are forwarded at the expense of an increase in the delay. The primary contributions of the proposed Adaptive Aggregation Routing (AAR) scheme are the following: (a) the senders select the forwarding node dynamically according to the length of the data queue, which effectively reduces the delay. In the AAR scheme, the senders send data to the nodes with a long data queue. The advantages are that first, the nodes with a long data queue need a small amount of data to perform aggregation; therefore, the transmitted data can be fully utilized to make these nodes aggregate. Second, this scheme balances the aggregating and data sending load; thus, the lifetime increases. (b) An improved AAR scheme is proposed to improve the QoS. The aggregation deadline (Tt) and the aggregation threshold (Nt) are dynamically changed in the network. In WSNs, nodes far from the sink have residual energy because these nodes transmit less data than the other nodes. In the improved AAR scheme, the nodes far from the sink have a small value of Tt and Nt to reduce delay, and the nodes near the sink are set to a large value of Tt and Nt to reduce energy consumption. Thus, the end to end delay is reduced, a longer lifetime is achieved, and the residual energy is fully used. Simulation results demonstrate that compared with the previous scheme, the performance of the AAR scheme is improved. This scheme reduces the delay by 14.91%, improves the lifetime by 30.91%, and increases energy efficiency by 76.40%.

An Adaption Broadcast Radius-Based Code Dissemination Scheme for Low Energy Wireless Sensor Networks

“…Sensor networks are being applied to all aspects of society, such as social networks [41,42,43,44], mobile networks [45], or forming the main body of the Internet of Things (IoT) [46,47,48,49], profoundly changing our ways of social interaction [42,47,48]. Energy is the most precious resource in wireless sensor networks [50,51].…”

Adaptive Transmission Range Based Topology Control Scheme for Fast and Reliable Data Collection

“…The paper [45] shows that appropriate use of in-network aggregation can significantly reduce the amount of traffic generated over the network. Data aggregation technology is a very important technology to reduce the data volume in wireless sensor networks [46,47]. There are many researches on the application of data aggregation in wireless sensor networks.…”

An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks

“…Nowadays, smart sensors based on IoT are playing a more and more fundamental role in many applications [ 28 , 29 ]. For example, they play a key role in long-term monitoring of a wide range of ecological environments [ 30 ], real-time monitoring and intelligent decision-making on traffic status [ 31 ], and are also widely used in agriculture, military, industrial automation, and many other applications [ 32 , 33 , 34 , 35 ]. However, there are several challenges related to the mass promotion for smart IoT applications, and some of these challenges are to make IoT work with low cost, as well as a green and an energy-efficient paradigm [ 36 , 37 , 38 ].…”

“…An important feature of energy consumption in wireless sensor networks is uneven energy consumption [ 51 , 52 ], because its data transmission is centered on the sink, which shows a many-to-one data collection feature. Therefore, the energy consumption around the sink is much higher than that of other regions in hotspots, and the energy consumption of the far sink region node is much smaller than that of hotspots, which causes premature death of hotspot nodes in ordinary WSNs, due to high energy consumption, thus forming a so-called “energy hole” and making the network die prematurely [ 11 , 30 , 36 ]. For EHWSNs, although energy can be absorbed to reduce energy stress, the imbalance of network energy consumption still has the following two effects on the network: (a) Since the energy consumption of nodes in a hotspot region is much higher than that in other regions, when the energy absorbed by the environment is insufficient to support energy consumption itself, the node will stop operating data to protect themselves (which is called outage of the nodes).…”