Energy-efficient and lifetime aware routing in WSNs

Network lifetime is an important performance metric in Wireless Sensor Networks (WSNs). Transmission Power Control (TPC) is a well-established method to minimise energy consumption in transmission in order to extend node lifetime and, consequently, lead to solutions that help extend network lifetime. The accurate lifetime estimation of sensor nodes is useful for routing to make more energy-efficient decisions and prolong lifetime. This research proposes an Energy-Efficient TPC (EETPC) mechanism using the measured Received Signal Strength (RSS) to calculate the ideal transmission power. This includes the investigation of the impact factors on RSS, such as distance, height above ground, multipath environment, the capability of node, noise and interference, and temperature. Furthermore, a Dynamic Node Lifetime Estimation (DNLE) technique for WSNs is also presented, including the impact factors on node lifetime, such as battery type, model, brand, self-discharge, discharge rate, age, charge cycles, and temperature. In addition, an Energy-Efficient and Lifetime Aware Routing (EELAR) algorithm is designed and developed for prolonging network lifetime in multihop WSNs. The proposed routing algorithm includes transmission power and lifetime metrics for path selection in addition to the Expected Transmission Count (ETX) metric. Both simulation and real hardware testbed experiments are used to verify the effectiveness of the proposed schemes. The simulation experiments run on the AVRORA simulator for two hardware platforms: Mica2 and MicaZ. The testbed experiments run on two real hardware platforms: the N740 NanoSensor and Mica2. The corresponding implementations are on two operating systems: Contiki and TinyOS. The proposed TPC mechanism covers those investigated factors and gives an overall performance better than the existing techniques, i.e. it gives lower packet loss and power consumption rates, while delays do not significantly increase. It can be applied for single-hop with multihoming and multihop networks. Using the DNLE technique, node lifetime can be predicted more accurately, which can be applied for both static and dynamic loads. EELAR gives the best performance on packet loss rate, average node lifetime and network lifetime compared to the other algorithms and no significant difference is found between each algorithm with the packet delay

Dynamic node lifetime estimation for wireless sensor networks

Wireless sensor networks (WSNs) consist of a large number of nodes each with limited battery power. As networks of these nodes are usually deployed unattended, network lifetime becomes an important concern. This paper proposes a novel, feasible, dynamic approach for node lifetime estimation that works for both static and dynamic loads. It covers several factors that have an impact on node lifetime, including battery type, model, brand, self-discharge, discharge rate, age, and temperature. The feasibility of the proposed scheme is evaluated by using the real testbed experiments with two wireless sensor platforms: Mica2 and N740 NanoSensor, two operating systems: TinyOS and Contiki, and different brands of alkaline and nickel-metal-hydride batteries. The deviation of the proposed estimation is in the range of -3.5% -2.5%. Three major contributions are presented in this paper: (1) the impact factors on node lifetime; (2) lifetime equations for any starting voltage, ageing, charge cycles, and temperatures; and (3) the dynamic node lifetime estimation technique, which is proposed and implemented on real hardware and software platforms in WSNs