BTA-MM: Burst traffic awareness-based adaptive mobility model with mobile sinks for heterogeneous wireless sensor networks

Using mobile sink increases the coverage time and energy expenditure when there is a burst traffic condition in wireless sensor networks (WSNs). Hence, it is essential to handle burst traffic using mobile sinks in an energy-efficient mobility manner. In most studies, mechanisms of the clustering and routing have not taken into account burst traffic. In fact, the number of studies with integrated mobile sink nodes, burst traffic awareness, multi-criteria cluster head (CH) selection, and mobile sink routing is negligible. For this purpose, a novel burst traffic awareness adaptive mobility scheme is proposed based on heterogeneous clustered WSNs, namely Burst Traffic Awareness-Mobility Model (BTA-MM). In the proposed scheme, the network area is first divided into two cluster groups. The CH selection is performed for each round by the average residual energy an d node load, taking into account the network coverage. An adaptive Gauss–Markov-burst traffic combination model is proposed in the study. In the proposed model, the mobile sinks collect all data in a single-hop communication as soon as they join the coverage intersection points (CIPs) of the CHs. The mobility model utilizes the adaptive minimum-weighted cost of the nodes. Once the burst packets are perceived in a CH, the data packets are backed up on the nearest and highest energy node in the cluster. Then, the mobile sink suddenly updates its trajectory towards the node, including the burst data traffic. Performance analysis of the proposed scheme was performed in NS-2 simulation environment. The most notable of the performance results is that the proposed method increased the network lifetime 42.5% more than any other method and also reduced the control overhead and average mobile path length by 72.5% and 35.9%, respectively. Also, the simulation results showed that the proposed method significantly reduced the average energy consumption 34.2% more than any other method, and increased the packet delivery rate 4.5% more than any other method, even in burst traffic. [Display omitted] •Proposing the multi-criteria CH selection algorithm.•Defining the adaptive burst-traffic aware mobility model in a heterogeneous clustered network.•Improving the Gauss–Markov-burst traffic mobility model.•Proposing the adaptive minimum weighted cost to find the coverage intersection points of the CHs.•Saving the burst data at the closest and highest energy node belong to the CHs.