Efficient Adaptive Beam Steering using Interference Normalization Least Mean Square Algorithm based Smart Antenna in Wireless Sensor Networks

Wireless Sensor Networks are increasingly being employed for monitoring and sensing in harsh environments such as factories and offshore platforms. This technology has the potential to offer measurements over larger and difficult to access areas, giving more up-to date and precise information to inform control and operational systems. Wireless sensor networks are ideal for the development of the envisioned world of ubiquitous and pervasive computing. Energy and computational efficiency constraints are the main key issues when dealing with this type of network. One of the challenges facing the development and adoption of wireless sensor networks is achieving wireless communications which is energy efficient yet robust and resilient. Being low cost and battery powered, wireless sensors have limited resources, which must be used optimally. Large Beam efficient smart antennas can give significant improvement in communications performance, and recent developments in parasitic array techniques have led to low power, low cost smart antennas. In this research, an interference-normalized least mean square (INLMS) algorithm for wireless sensor network is proposed. The INLMS algorithm extends the gradient-adaptive learning rate approach to the case where the signals are non-stationary, we also compare the performance of proposed smart antenna scheme with existing LMS and NLMS algorithms. Comparison is done through the performance matrices like, Beam-width, throughput, End-to-End Delay, Network Lifetime etc. and it is found that wireless sensor network with proposed smart antenna scheme out perform the existing approaches.