Applied Soft Computing 29 (2015) 12–25 Contents lists available at ScienceDirect Applied Soft Computing j ourna l ho me page: www.elsevier.com/locate /asoc Heuristic routing with bandwidth and energy constraints in sensor networks S. Kavi Priya a,∗ , T. Revathi b , K. Muneeswaran c , K. Vijayalakshmi d a Mepco Schlenk Engineering College (Autonomous), Sivakasi, India b Department of IT, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India c Department of CSE, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India d Department of CSE, Ramco Institute of Technology, Rajapalayam, India a r t i c l e i n f o Article history: Received 20 August 2011 Received in revised form 15 October 2014 Accepted 15 December 2014 Available online 29 December 2014 Keywords: Sensor networks routing Bandwidth constraint Energy constraint Nearest neighbor tree Distributed algorithm Maximum lifetime a b s t r a c t Most of the routing algorithms devised for sensor networks considered either energy constraints or band- width constraints to maximize the network lifetime. In the real scenario, both energy and bandwidth are the scarcest resource for sensor networks. The energy constraints affect only sensor routing, whereas the link bandwidth affects both routing topology and data rate on each link. Therefore, a heuristic technique that combines both energy and bandwidth constraints for better routing in the wireless sensor networks is proposed. The link bandwidth is allocated based on the remaining energy making the routing solu- tion feasible under bandwidth constraints. This scheme uses an energy efficient algorithm called nearest neighbor tree (NNT) for routing. The data gathered from the neighboring nodes are also aggregated based on averaging technique in order to reduce the number of data transmissions. Experimental results show that this technique yields good solutions to increase the sensor network lifetime. The proposed work is also tested for wildfire application. © 2014 Elsevier B.V. All rights reserved. 1. Introduction Wireless sensor network consists of large number of tiny sensor nodes connected via wireless communication channels. These are suitable for lots of appli- cations such as military surveillance, temperature monitoring, wildfire detection, disaster warning, etc. In particular, sensors are deployed to monitor the regions where the human cannot intervene. For instance, sensors deployed for wildfire detection in the forest region continuously monitors the environment to detect the changes in temperature. When the temperature value crosses the threshold value say 40 ◦ C (event detection), sensor routes the data to sink node (typically a base station or a sensor/actuator node or a gateway to larger network with high comput- ing power and energy where information is required) in the remote location through the multi-hop routing algorithms. Therefore, the sink collects the data from all the sensor nodes to derive useful information about the event (for example the geo- graphical map of the wildfire can be plotted) detected. Fig. 1 shows the model of wireless sensor networks used in the proposed work. According to the characteris- tics of sensor network, the sensor nodes performsensing, preprocessing, aggregation and transmission of data on its neighboring nodes within the transmission range. Hence, the total data rate increases suddenly in the sensor networks when it detects the event. The sensor data cannot be further forwarded to the neighboring node, if the sensor node runs out of energy or due to network congestion. The sensor network ∗ Corresponding author at: Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India. Tel.: +91 9842295563; fax: +91 04562235111. E-mail addresses: urskavi@mepcoeng.ac.in (S. Kavi Priya), trevathi@mepcoeng.ac.in (T. Revathi), kmuni@mepcoeng.ac.in (K. Muneeswaran), vijayasrini9701@gmail.com (K. Vijayalakshmi). starts to congest when the total link bandwidth between the sensor nodes is smaller than the data rate of the network. Hence the wireless sensor networks are consid- ered as resource scarce, which is manifested in terms of energy, link bandwidth, computing power, etc. In most of the previous works related to sensor networks, the authors tried to increase either energy efficiency through different routing tech- niques [1–10] or optimize wireless link bandwidth as in [11,12]. The classical routing algorithms like minimum spanning tree [13,14], requires calculation of routing path at every node and results in high computing power to find the optimal path. The use of the distributed algorithm to find the best optimal nearest neighbors for packet forwarding will increase the network’s lifetime. The network lifetime is considered as the time until which the first node in the sensor network drains out of energy. When every sensor node is allowed to forward data only to the nearest next neigh- boring node with optimal performance factor (energy or bandwidth efficiency) along with data aggregation (that converges number of data received from various sources into few messages), the sensor network’s lifetime will be maximized as discussed in [15–17]. In [18], the authors have devised a routing technique with both energy and link constraints which will have performance degradation since it is executed in a centralized fashion. In some of the recent works [22–24], energy efficiency is attained by increasing the network coverage (resulted in increased hardware cost), standby cluster head (suffered due to central point of failure if cluster node is dead) and efficient location discovery respectively. The researchers also concluded that the distributed routing algorithm may increase the sensor network’s lifetime. The works proposed in [2,5,9,15,17], suggests that using data aggregation in sensor net- work can utilize bandwidth efficiently. The survey of the papers [25–27] reveals that the performance of the sensor network may also depend on the type of application for which it is used. Therefore, this work proposes a model to tackle bandwidth constraints using link rate allocation and energy constraints using distributed NNT algorithm along with data aggregation considering the issues in the wireless sensor network wildfire application. http://dx.doi.org/10.1016/j.asoc.2014.12.019 1568-4946/© 2014 Elsevier B.V. All rights reserved.