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An intrabody area nanonetwork (intra-BANN) is a set of nanoscale devices, which have outstanding cellular level precision and accuracy for enabling noninvasive healthcare monitoring and disease diagnosis. In this article, we design a novel feedforward neural networks (FFNNs) based data aggregation scheme that integrates the attributes of artificial intelligence to boost the computational intelligence of intra-BANNs for prolonged network lifetime. In the proposed scheme, data division and labeling are performed to transmit detected information using two different types of packets with different sizes to save energy resources and to avoid redundant data transmission. FFNN-based periodic data transmission exploits the fitness function approximation characteristics of FFNN to increase the transmission probability of critical information with minimum energy consumption and delay, whereas our proposed event-driven data transmission also ensures the transmission of high priority data with minimal delay and storage overhead. The detailed evaluation and comparison of our proposed framework with three existing schemes conducted using the Nano-Sim tool highlight that our proposed scheme performs 50%–60% better than state-of-the-art schemes in terms of residual energy, delay, and packet loss. more...

The network switches and routers need to forward the network packets at a line-rate. The underlying hardware and software implementations may limit the performance of these packet processing devices. In this paper, we propose a novel model, called Lookaside , to increase the performance of the network switches by extracting the payload from the packet and storing it in a nearby standard memory, such as dynamic random access memory (DRAM), before the packet is sent for processing. We know that the packet payload has no role in packet processing. We implemented the prototypes of our Lookaside switch model on the state-of-the-art ONetSwitch45 hardware field-programmable gate array board. The simulation results show an increase in the performance of the switch in terms of latency, throughput, switching chip resources, and on-chip resources power consumption as compared to SimpleSwitch. more...

Today, data center networks (DCNs) are built using multi-tier architecture. These large-scale networks face many challenges, such as security, delay, low throughput, loops, link oversubscription, TCP Incast and Outcast, etc. In this paper, a TCAM (Ternary Content Addressable Memory) based routing technique is proposed, augmenting the routing capabilities of multi-tier architectures in large scale networks. The routing complexities in these architectures are rectified and improved by implementing an additional TCAM based routing table in Leaf/ToR switches for a specific number of compute nodes in particular Pods, and it is scalable to whole datacenter nodes. To test the model, we implemented two prototype models: one depicting our proposed TCAM based switch and the other is a typical Top-of-the-Rack (ToR) switch and compared the performance of the proposed model and if any overhead introduced in it. The preliminary results show that our TCAM based routing table technique is fast and it forwards the network packets at line-rate, does not introduce considerable latency, on-chip resources power consumptions is less than 3%, and helps to solve or mitigate the above critical problems that are present in the current large DC's three-tier architecture, especially in Top of the Rack and aggregation layers switches. more...

An intrabody nanonetwork (IBNN) is composed of nanoscale (NS) devices, implanted inside the human body for collecting diverse physiological information for diagnostic and treatment purposes. The unique constraints of these NS devices in terms of energy, storage and computational resources are the primary challenges in the effective designing of routing protocols in IBNNs. Our proposed work explicitly considers these limitations and introduces a novel energy-efficient routing scheme based on a fuzzy logic and bio-inspired firefly algorithm. Our proposed fuzzy logic-based correlation region selection and bio-inspired firefly algorithm based nano biosensors (NBSs) nomination jointly contribute to energy conservation by minimizing transmission of correlated spatial data. Our proposed fuzzy logic-based correlation region selection mechanism aims at selecting those correlated regions for data aggregation that are enriched in terms of energy and detected information. While, for the selection of NBSs, we proposed a new bio-inspired firefly algorithm fitness function. The fitness function considers the transmission history and residual energy of NBSs to avoid exhaustion of NBSs in transmitting invaluable information. We conduct extensive simulations using the Nano-SIM tool to validate the in-depth impact of our proposed scheme in saving energy resources, reducing end-to-end delay and improving packet delivery ratio. The detailed comparison of our proposed scheme with different scenarios and flooding scheme confirms the significance of the optimized selection of correlated regions and NBSs in improving network lifetime and packet delivery ratio while reducing the average end-to-end delay. more...

Wireless multimedia sensor networks (WMSNs) have emerged as a revolutionary technology, that has shifted the focus of low power wireless sensor networks (WSNs). These networks are aimed at gathering and delivering both scalar and multimedia data from the environment. WMSNs inherit the miniature size, low power, low processing and short range wireless communication traits from WSNs. Addition of multimedia content in WMSNs demands fulfillment of various QoS parameters like low end-to-end delay, acceptable jitter rate, low packet loss rate and higher throughput. Over the past few years, considerable research efforts have been directed towards the fulfillment of these QoS requirements; by proposing new traffic-aware medium access and routing algorithms. However, the issues of rate adaptation and congestion control are still largely unexplored. Congestion in wireless networks is a major cause of packet loss resulting in degraded network performance. In WMSNs congestion is a common occurrence, due to the communication of high data rate, bursty video traffic over lossy wireless link. Therefore, it is critical to adjust the sending rate of source nodes based on network conditions. This work investigates the issue of rate adaptation and congestion control in WMSNs. A traffic-aware congestion control protocol (TACC) is proposed that operates on end-to-end principle at the transport layer. The proposed protocol uses burst loss information to detect congestion at the destination and directs source nodes to adjust reporting rate accordingly. The proposed protocol is evaluated using simulation analysis which shows significant improvement in terms of packet losses, end-to-end delay, packet delivery ratio and received picture quality. more...

Wireless communication for nano-nodes which can be implanted will revolutionize area of health-care and disease detection system. Electromagnetic (EM) waves spread in biological tissues and its diffusion affects molecular absorption which results in thermal effects. When implanted nano-nodes are increased, communication among nano-nodes is also increased that can lead to a temperature rise in surrounding biological cells. Our aim is to analyze the affect of nano-node density on heat generation. Simulations are conducted using NanoSim tool for analyzing the temperature rise due to node density. Results shows by increasing nodes density, the body temperature will also increased due to the high rate of packet generation and packet forwarding. more...