A cooperation-based approach to energy optimization in wireless ad hoc networks

A well known and still open issue for wireless ad hoc networks is the unfair energy consumption among the nodes. The specific position of certain nodes composing an ad hoc network makes them more involved in network operations than others, causing a faster drain of their energy. To better distribute the energy level and increase the lifetime of the whole network, we propose to periodically force the cooperation of less cooperative nodes while overwhelmed ones deliberately stop their service. A dedicated ad hoc network routing protocol is introduced to discover alternative paths without degradation in the overall network performance

Graded Reliance Based Routing Scheme for Wireless Sensor Networks

In this paper Graded Reliance based routing algorithm is proposed to deal with defective nodes in Wireless Sensor Networks (WSN’s).The algorithm is intended to validated or build evidence that, by dynamically learning from previous experience and adapting the changes in the operational environment the application performance can be maximized and also enhance operative agility. Quality of service and social network measures are used to evaluate the confidence score of the sensor node. A dynamic model-based analysis is formulated for best reliance composition, aggregation, and formation to maximize routing performance. The results indicate that reliance based routing approaches yields better performance in terms of message delivery ratio and message delay without incurring substantial message overhead

Active Topology Inference using Network Coding

Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking

A short survey on fault diagnosis in wireless sensor networks

Fault diagnosis is one of the most important and demand- able issues of the network. It makes the networks reliable and robust to operate in the normal way to handle almost all types of faults or failures. Additionally, it helps sensor nodes to work smoothly and efficiently till the end of their lifetime. This short survey paper not only presents a clear picture of the recent proposed techniques, but also draws comparisons and contrasts among them to diagnose the potential faults. In addition, it proposes some potential future-work directions which would lead to open new research directions in the field of fault diagnosis

A Machine Learning SDN-Enabled Big Data Model for IoMT System

[EN] In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.Haseeb, K.; Ahmad, I.; Iqbal Awan, I.; Lloret, J.; Bosch Roig, I. (2021). A Machine Learning SDN-Enabled Big Data Model for IoMT System. Electronics. 10(18):1-13. https://doi.org/10.3390/electronics10182228S113101