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In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is “sliding” forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.

An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme.

In mobile ad hoc networks, cooperation among active mobile nodes is considered to play a vital role in reliable transmission of data. But, the selfish mobile nodes present in an ad hoc environment refuse to forward neighbouring nodes’ packet for conserving its own energy. This intentional selfish behaviour drastically reduces the degree of cooperation maintained between the mobile nodes. Hence, a need arises for devising an effective mechanism which incorporates both energy efficiency and reputation into account for mitigating selfish behaviour in MANETs. In this paper, we propose an Exponential Reliability Coefficient based reputation Mechanism (ERCRM) which isolates the selfish nodes from the routing path based on Exponential Reliability Coefficient (ExRC). This reliability coefficient manipulated through exponential failure rate based on moving average method highlights the most recent past behaviour of the mobile nodes for quantifying its genuineness. From the simulation results, it is evident that, the proposed ERCRM approach outperforms the existing Packet Conservation Monitoring Algorithm (PCMA) and Spilt Half Reliability Coefficient based Mathematical Model (SHRCM) in terms of performance evaluation metrics such as packet delivery ratio, throughput, total overhead and control overhead. Further, this ERCRM mechanism has a successful rate of 28% in isolating the selfish nodes from the routing path. Furthermore, it also aids in framing the exponential threshold point of detection as 0.4, where a maximum number of selfish nodes are identified when compared to the existing models available in the literature.