The technological advancements of Internet of Things (IoT) has revolutionized traditional Consumer Electronics (CE) into next-generation CE with higher connectivity and intelligence. This connectivity among sensors, actuators, appliances, and other consumer devices enables improved data availability, and provides automatic control in CE network. However, due to the diversity, decentralization, and increase in the number of CE devices the data traffic has increased exponentially. Moreover, the traditional static network infrastructure-based approaches need manual configuration and exclusive management of CE devices. Motivated from the aforementioned challenges, this article presents a novel Software-Defined Networking (SDN)-orchestrated Deep Learning (DL) approach to design an intelligent Intrusion Detection System (IDS) for smart CE network. In this approach, we have first considered SDN architecture as a promising solution that enables reconfiguration over static network infrastructure and handles the distributed architecture of smart CE network by separating the control planes and data planes. Second, an DL-based IDS using Cuda-enabled Bidirectional Long Short-Term Memory (Cu-BLSTM) is designed to identify different attack types in the smart CE network. The simulations results based on CICIDS-2018 dataset support the validation of the proposed approach over some recent state-of-the-art security solutions and confirms it a phenomenal choice for next-generation smart CE network.

One of the major challenges in design of wireless sensor networks (WSNs) is to reduce energy consumption of sensor nodes to prolong lifetime of finite capacity batteries. In this paper, we propose energy-efficient adaptive scheme for transmission (EAST) in WSNs. EAST is an IEEE 802.15.4 standard compliant. In this scheme, open-looping feedback process is used for temperature-aware link quality estimation and compensation, wherea closed-loop feedback process helps to divide network into three logical regions to minimize overhead of control packets. Threshold on transmitter power loss (${\text{RSSI}}_{\text{loss}}$ ) and current number of nodes ($n_c$ (t)) in each region help to adapt transmit power level ($P_{\text{level}}$ ) according to link quality changes due to temperature variation. Evaluation of the proposed scheme is done by considering mobile sensor nodes and reference node both static and mobile. Simulation results show that the proposed scheme effectively adapts transmission $P_{\text{level}}$ to changing link quality with less control packets overhead and energy consumption as compared to classical approach with single region in which maximum transmitter $P_{\text{level}}$ assigned to compensate temperature variation.

Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications.

Constant monitoring of patients without disturbing their daily activities can be achieved through mobile networks. Sensor nodes distributed in a home environment to provide home assistance gives concept of Wireless Wearable Body Area Networks. Gathering useful information and its transmission to the required destination may face several problems. In this paper we figure out different issues and discuss their possible solutions in order to obtain an optimized infrastructure for the care of elderly people. Different channel models along with their characteristics, noise filtering in different equalization techniques, energy consumption and effect of different impairments have been discussed in our paper. The novelty of this work is that we highlighted multiple issues along with their possible solutions that a BAN infrastructure is still facing.