Recognizing human activity in multi-resident smart homes is complex due to sparse sensor activations and overlapping occupant actions. This paper presents a Parallel Lightweight Hybrid Attention BiGRU framework, integrating Multi-Head Attention (MHA) with Bidirectional GRUs (BiGRUs) to address these challenges effectively. The model processes global sensor relationships and local temporal dependencies in parallel, overcoming information loss typical in sequential processing. Experiments on real world smart home ARAS datasets show that the framework achieves 97.99% accuracy in Multi resident settings in House A and 99.49% accuracy in multi resident scenarios in House B. It also generalizes well across different households, demonstrating strong adaptability and robustness. By combining attention mechanisms with recurrent neural networks, the proposed architecture efficiently captures key patterns in sparse and concurrent sensor data. This work marks a significant advancement in multi resident human activity recognition, offering a scalable and reliable solution for real world smarthome applications and establishing a strong foundation for future research in pervasive computing and ambient intelligence systems.

The industrial Internet of things (IIoT) relies heavily on real-time, energy-efficient, and reliable wireless communication. Although the IEEE 802.15.4-based medium access control (MAC) protocols, such as deterministic synchronous multichannel extension (DSME) enhances multichannel access for constrained IIoT environments, it lacks robust resource allocation mechanisms for guaranteed time slot (GTS) scheduling and adaptive channel assignment under quality of service (QoS) constraints. To address these limitations, we propose STREAM-E, a QoS- and energy-aware MAC protocol that enhances the performance of DSME MAC in dynamic industrial settings. STREAM-E integrates QoS-aware GTS scheduling based on traffic class and buffer occupancy, exponentially weighted moving average (EWMA)-based dynamic channel selection to reduce interference, and (iii) a genetic algorithm (GA)-based energy-aware mechanism to extend network lifetime during emergencies. We assessed STREAM-E through performance evaluation in a cluster-tree network and detailed cost analysis. The simulation results show that STREAM-E improves overall resource utilization by 9.23% compared to existing DSME-based MAC protocols. Moreover, it achieves 20.04% more energy efficiency during emergencies.

In this article, we propose a method to solve the problem of optimal distribution of slots among sensor devices associated in wireless body area networks (WBANs). Modern healthcare is witnessing a paradigm shift due to the recent developments in Internet of Things (IoT), and WBAN is a fundamental enabling component of IoT-based healthcare. In a WBAN, sensors and actuators are implanted in patients (both on-body and in-body) connected to a hub to monitor health conditions ubiquitously and in real time. WBANs deal with heterogeneous sensors with diverse resource demands and constraints. Optimal allocation of data transmission slots among these heterogeneous sensors is a real challenge. Thus, in this article, we propose a cooperative game-theoretic approach, based on the Nash bargaining solution (NBS), for allocating slots for sensor devices in WBANs where the sensors communicate with each other following the IEEE 802.15.6 standard. We also compare the performance of our work with this standard and other relevant benchmarks to show the efficacy of the proposed solution. The proposed bargaining-based optimal slot sharing (BOSS) algorithm yields 26.68% better reliability and 38.07% better throughput, on an average, than the traditional IEEE 802.15.6 standard.

Wireless body area networks (WBANs) support various types of medical applications with heterogeneous requirements. Therefore, we need to use an efficient medium access control (MAC) protocol to ensure reliable data transmission. In this article, we propose a dynamic superframe structure-based MAC protocol extending the principles of the IEEE 802.15.6 standard. In this work, to allocate dedicated slots for each sensor device, a prioritized dedicated slot allocation mechanism using the criteria importance through intercriteria correlation (CRITIC) is proposed. With the help of this method, the priority value of sensor devices is calculated based on different sensors’ parameters. We compared the performance of our proposed work with standard IEEE 802.15.6 MAC and a few other MAC protocols. The simulation result shows that our proposed MAC protocol performed better in terms of energy efficiency and reliability, as well as reducing the packet drop probability. Results show that the reliability of data transmission increases over the IEEE 802.15.6 MAC protocol by more than 50%.

In this paper, we propose an algorithm for Context-Aware REgulation of backoff delay (d-CARE) in delay-sensitive networks that follow IEEE 802.15.4 standard. The slotted Carrier Sense Multiple Access - Collision Avoidance (CSMA-CA) algorithm prescribed in the standard, to resolve the contention over channel access, does not select the initial Backoff Exponent (BE) value judiciously. In other words, it cannot differentiate between delay-sensitive and delay-tolerant applications. Thus, context-less selection of BE may lead towards a high delay for delay-sensitive applications, for example - real time health monitoring of patients. The proposed d-CARE algorithm uses a Fuzzy Inference System (FIS) to address this problem. As a case study, contexts or attributes that are relevant to healthcare applications are considered as the inputs to the FIS, which generates a BE value as the corresponding output. This judiciously selected BE value produces 12% less delay than the traditional approach.

In this paper, we analyze the superframe structure of the Medium Access Control (MAC) sublayer of IEEE 802.15.4 protocol (ZigBee), designed for Low-Rate Wireless Personal Area Networks (LR-WPANs), and evaluate the effects of the inactive portion of a superframe on average delay, and average power consumption. The four-dimensional Markov chain-based analysis of the slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithm presented in this work considers backoff freezing and acknowledged packet transmission that are not studied in the existing works. The analytical results prove that the performance of LR-WPANs depends significantly on the length of a superframe's active portion. We introduce a variable-Superframe duration-Beacon interval Ratio (SBR), which is utilized by tuning a few MAC parameters to achieve 35 percent reduced delay, on an average, compared to the existing state of the art. The results show that the proposed model also yields improved performance in terms of power consumption, for short and medium contention windows. In addition to the proposed analysis, this work provides optimized superframe order values that achieve trade-offs between delay and power consumption as demanded by user-provided QoS requirements corresponding to different contexts.

In wireless sensor networks, adaptive tuning of Medium Access Control (MAC) parameters is necessary in order to assure the QoS requirements. In this paper, we propose an adaptive MAC-frame payload tuning mechanism for wireless body area networks (WBANs) to maximize the probability of successful packet delivery or reliability of the associated sensor nodes based on real-time situation. The enabling algorithm, Adaptively Tuned MAC (AT-MAC), has been proposed to tune the MAC-frame payload of a WBAN sensor node, which is compliant with the IEEE 802.15.4 protocol. AT-MAC prioritizes sensor nodes based on the seriousness of the health parameters that are being measured by the respective sensor nodes. Further, we consider a Markov chain-based analytical approach that acknowledges the slotted CSMA/CA backoff mechanism with retry limits, as described in the IEEE 802.15.4 protocol. We derive expressions for reliability, power consumption, and throughput, which are the key metrics to evaluate the network performance of the proposed protocol, and analyze the impact of MAC parameters on them. Finally, results indicate that the low rate and low power IEEE 802.15.4 can be used effectively in case of WBANs if the payload is tuned properly through the proposed algorithm. The proposed AT-MAC algorithm yields around 70 percent increase in reliability of a critical node in a WBAN.

This paper presents a bargaining-based resource allocation and price agreement in an environment of cloud-assisted Wireless Body Area Networks (WBANs). The challenge is to finalize a price agreement between the Cloud Service Providers (CSPs) and the WBANs, followed by a cost-effective mapping among them. Existing solutions primarily focus on profits of the CSPs, while guaranteeing different user satisfaction levels. Such pricing schemes are bias prone, as quantifying user satisfaction is fuzzy in nature and hard to implement. Moreover, such an traditional approach may lead to an unregulated market, where few service providers enjoy the monopoly/oligopoly situation. However, in this work, we try to remove such biasness from the pricing agreements, and envision this challenge from a comparatively fair point of view. In order to do so, we use the concept of bargaining, an interesting approach involving cooperative game theory. We introduce an exposition - multi-stage Nash bargaining solution (MUST-NBS), that unfolds into multiple stages of bargaining, as the name suggests, until we conclude price agreement between the CSPs and the WBANs. In addition, the proposed approach also consummates the final mapping between the CSPs and the WBANs, depending on the cost-effectiveness of the WBANs. Analysis of the proposed algorithms and the inferences of the results validates the usefulness of the proposed mapping technique.

In this paper, we propose a cooperative game theoretic approach for data-rate tuning among sensors in a Wireless Body Area Network (WBAN). In a WBAN, the body sensor nodes implanted on a human body typically communicate through a capacity-constrained single channel. This is a serious concern because most applications in WBANs involve real-time data streaming and providing useful notifications and efficient feedback to the patients or other users according to their health conditions. To increase the Quality of Service (QoS), we need an efficient data-rate tuning mechanism, which tunes the data-rate of a sensor based on the criticality of health parameter measured through it. Our approach considers the unique features typical of WBAN applications, and provides a generalized solution for the problem. We propose a cooperative game theoretic approach, based on the Nash Bargaining Solution (NBS), which does not only provide priority-based tuning, but also maintains the fairness axioms of game theory. The proposed approach yields 10% average increase in data-rates for the sensor nodes that have critical physiological data to transmit. We also validate the approach through real system implementation with the help of real sensor devices such as heart rate sensor, and pulse oximeter.

This paper presents a priority-based MAC-frame payload tuning mechanism with reduced energy consumption for healthcare systems that use Wireless Body Area Networks (WBANs). A fundamental problem in WBANs is to prioritize the physiological sensors depending on several health and external criteria. The challenge is to design a dynamic decision making model that can optimize the energy consumption of each physiological sensor. To address this problem we employ the concept of Fuzzy Inference System (FIS) in order to calculate Criticality Index (CI), which signifies the severity or the priority of the physiological data sensed by each sensor. Considering the obtained CI value we proceed with designing a Markov Decision Process (MDP) based dynamic decision making model in order to tune MAC-frame payload by optimizing the energy consumption of each sensor node. We achieve around 25% decrease in the overall energy consumption using our proposed mechanism.