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Summary Bandwidth estimation in mobile ad hoc networks (MANET), where each node can move randomly and is capable of frequently changing its link with other nodes, is a challenging task. Motivation of this work is in contrast with TCP new‐reno which decreases the congestion window both in the event of link failure and congestion, which in the case of packet loss due to link failure should be close to available channel bandwidth. The proposed novel approach capture the node's mobility behavior in broadcast and unicast scenarios of IEEE 802.11 standard to efficiently estimate the sender's window size. This proposal introduces a data structure and source‐to‐destination path stability metric to imitate the mobility behavior of network and presents the analytic characterization of steady‐state throughput as a function of packet loss, round trip time, and path stability over IEEE 802.11 infrastructure‐less MANET. The performance is evaluated over random‐walk, random‐waypoint, and Gauss‐Markov mobility models in 2D and 3D environments using QualNet 7.4 network simulator. The proposed analytical model is also evaluated through two‐tailed statistical test. Analytical, statistical, and simulation‐based comparisons demonstrate the effectiveness of proposed method in high‐mobility scenarios.

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This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

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