Towards a Formal Validation of a Hybrid Multi-Agent Routing Protocol for Ad hoc network

In our previous papers, a new Ant Routing Protocol for Ad-hoc Networks inspired from ant colony optimization was presented. We introduced a new approach which decreases both of nodes energy consumption and routing overhead within the network. The validation of our routing protocol was based on series of simulation. The results show that our new algorithm provides a significant improvement compared to other protocols. After the algorithm is defined and published, we have found important to validate formally each one of its components in order to avoid any conflict, lack or misbehaving situations. This process requires in a first step a formal specification. This is our main concern in this paper where we propose in a first part a formal specification using inference systems based on logical rules. A formal validation using these inference systems is proposed in a second step in order to prove the correctness, the soundness, the completeness and the optimality of the proposition.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

Adaptive-Sunflower Based Grey Wolf Algorithm For Multipath Routing In Iot Network

This paper devises a routing method for providing multipath routing inan IoT network. Here the Fractional Artificial Bee colony(FABC)algorithm is devised for initiating clustering process. Moreover the multipath routing is performed by the newly devised optimization technique, namely Adaptive-Sunflower based grey wolf(Adaptive-SFG)optimization technique which is designed by incorporating adaptive idea in Sunflower based grey wolf technique. In addition the fitness function is newly devised by considering certain factors that involves Context awareness, link lifetime Energy, Trust, and Delay.For the computation of the trust, additional trust factors like direct trust indirect trust recent trust and forwarding rate factor is considered. Thus, the proposed Adaptive SFG algorithm selects the multipath for routing based on the fitness function.Finally, route maintenance is performed to ensure routing without link breakage.The proposed Adaptive-SFG outperformed other methods with high energy of0.185Jminimal delay of 0.765sec maximum throughput of47.690%and maximum network lifetime of98.7%.

Wireless Body Area Networks for Healthcare

Motivated by the increasing need for improved healthcare solutions, Wireless Body Area Networks (WBANs) have shown their great potential in revolutionizing the next generation healthcare through enabling continuous monitoring of health status with early detection of abnormal situations. Such networks are able to support a diverse range of applications with traffic rates ranging from several bits per hour up to 10 megabits per second. For the efficient functionality of these applications, each one poses a specific set of Quality of Service (QoS) requirements to the Medium Access Control (MAC) sub-layer including transmission reliability, timeliness and throughput. However, energy limitations of WBANs make the satisfaction of these requirements a challenging task. The current paper aims to explore the application trends of WBANs in the health field as well as the salient features of the MAC protocols proposed for this class of networks, and to provide a general rule indicating the most suitable MAC technology for WBANs based on the characteristics of the targeted application.

Network Slice Provisioning Approaches for Industry Verticals

Network slicing is widely studied as an essential technological enabler for supporting diverse use case specific services through network virtualization. Industry verticals, consisting of diverse use cases requiring different network resources, are considered key customers for network slices. However, different approaches for network slice provisioning to industry verticals and required business models are still largely unexplored and require further work. Focusing on technical and business aspects of network slicing, this article develops three new business models, enabled by different distributions of business roles and management exposure between business actors. The feasibility of the business models is studied in terms of; the costs and benefits to business actors, mapping to use cases in various industry verticals, and the infrastructure costs of common and dedicated virtualization infrastructures. Finally, a strategic approach and relevant recommendations are proposed for major business actors, national regulatory authorities, and standards developing organizations.

Protruder optimization-based routing protocol for energy efficient routing in Wireless Sensor Networks

The energy efficiency problem is addressed using the Cluster Head (CH) formation, data aggregation, and routing techniques. Therefore,an energy-aware routing algorithm named as protruder optimization algorithm is proposed, which boosts the network lifetime through finding the optimal routing path. The proposed protruder optimization is developed with the hybridization of the wave propagator characteristics and weed characteristics in such a way that the global optimal convergence is boosted while selecting the optimal routing path. Moreover, the communication in the network through the optimal path is progressed through the optimal CHs selection based on fractional artificial bee colony optimization (FABC) and in turn, the energy minimization problem is aided with data aggregation process using sliding window approach that avoids retransmission of the data. The results of the proposed method are compared with the existing methods on the basis of its performance measures such as energy, alive nodes and throughput.

A new cooperation model for dynamic networks

Technologies evolution revealed new types of dynamic networks with decentralized architectures and autonomous services. Research on this impressive area has provided great objectives and benefits. However, providing some services related to security and routing protocols are a major problem in this domain. All nodes in the networks need to cooperate and relay packets for other nodes, but some misbehavior nodes due to selfish reasons may significantly reduce the network performances. Because they use the network resources only for their own purpose and not share them between neighbors. In this paper, a novel technique of enforcement cooperation is proposed. It aims to control the role of each node in the network and evaluate their participation during the routing function. The model includes important features that force nodes cooperating and discard the selfish ones. Simulation results showed that the proposed model is very efficient to detect and remove misbehavior nodes and enhance cooperation between nodes while routing data.

Disaster Relief Management Using Reinforcement Learning-Based Routing

Effective disaster management is required for the peoples who are trapped in the disaster scenario but unfortunately when disaster situation occurs the infrastructure support is no longer available to the rescue team. Ad hoc networks which are infrastructure-less networks can easily deploy in such situation. In disaster area mobility model, disaster area is divided into different zones such as incident zone, casualty treatment zones, transport areas, hospital zones, etc. Also, in order to tackle high mobility of nodes and frequent failure of links in a network, there is a need of adaptive routing protocol. Reinforcement learning is used to design such adaptive routing protocol which shows good improvement in packet delivery ratio, delay and average energy consumed.

Intent-Based Network Policy to Solution Architecting Recommendations

The semantic diversity of policies written by people with different IT literacy to achieve certain network security or performance goals created ambiguity to otherwise straightforward solution implementations. In this project, an intent-aware solution recommender is designed to decode semantic cues in network policies written by various demographics for robust solution recommendations. A novel policy analyzer is designed to extract the intrinsic networking intents from ICT policies to provide context-specific solution recommendations. A custom network-aware intent recognizer is trained on a small keywords-to-intents dataset annotated by domain experts using NLP algorithms in AWS comprehend. The bin-of-words model is then used to classify sentences in the policies into predicted ‘intent' class. A collaborative filtering recommendation system using crowd-sourced ground-truth is designed to suggest optimal architecting solutions to achieve the requirements outlined in ICT policies.

Harris Hawks Jaya Algorithm-Based Routing Protocol in Delay Tolerant Network

This paper introduces a trust-based multipath routing protocol for exploiting different paths between source as well as destination to mitigate energy constraints. The key idea is to determine optimal path from the entire paths available among source and target node. To improve the security in routing protocol, the factors, like trust factors, and distance are considered as major components. Based on these parameters, the multipath routing is carried out based on HH-Jaya Algorithm. The HH-Jaya is designed newly by integrating Harris Hawks Optimization (HHO) and Jaya Algorithm. After that, the reputation and trust-based context aware routing (RCAR) protocol is utilized to select the optimal path with more trust factor. Here, the trust is modelled by considering trust factors, like direct, indirect, history, forwarding rate, and availability factors, in addition to the utility function. The proposed HH-Jaya outperformed other methods with minimal delay of 0.007 sec, maximal throughput of 0.913 for 10 user and maximal packet deliver rate (PDR) of 0.991 for 20 users respectively.