The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.

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An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare

ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversarial WSNs

Internet of Things (IoT) has gained immense attention over the last decade. Even though IoT has great potential, several issues like security, energy optimization, data storage, real time data analytics, etc. have hindered the realization of true potential of IoT. As the sensors in the IoT network continuously monitor the environment, the longevity of the IoT networks is affected. Many researchers have tried to solve the issue of network longevity/energy optimization in IoT networks in the past few years. Optimizing the energy in IoT networks is still undergoing research. One of the methods used to optimize the energy in IoT networks is through selecting the optimal Cluster Head (CH) in the IoT networks. In this paper we have made an attempt to optimize the energy utilization in the IoT networks through an optimal CH selection using recently developed nature inspired algorithm, namely, Harris Hawks Optimization algorithm (HHO). The performance of the HHO-based CH model is analyzed through several metrics such as delay, load, number of alive nodes, residual energy, and temperature. The experimental results prove that the proposed HHO-based CH selection method performs better than the state-of-the-art CH selection models. The proposed model extends the network’s lifetime by keeping more alive nodes even after the $3000^{\textit {th}}$ round. The proposed model retains thirty nodes, genetic algorithm (GA) retains one node, artificial bee colony algorithm (ABC) retains three nodes, moth-flame optimization algorithm (MFO) retains seven nodes, and whale optimization algorithm (WOA) retains eleven nodes.

Energy Optimization for Green Communication in IoT Using Harris Hawks Optimization

Futuristic view of the Internet of Quantum Drones: Review, challenges and research agenda

Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature’s ecosystems. Bio-inspired algorithms’ adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges, such as Identity and Authentication, Access Control Systems, Protocol and Network Security, Trust Management, Intrusion Detection, Virtualization, and Forensic.

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Applications and Evaluations of Bio-Inspired Approaches in Cloud Security: A Review

Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.

Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks

One of the major bottlenecks toward realizing IoT systems is the energy constraint of sensors. Prolonging network lifetime is a fundamental issue for implementing IoT systems. The energy optimization problem, being NP-hard in nature for scalable networks, has been addressed in the literature using traditional metaheuristic techniques. Quantum inspired metaheuristics have shown better performance than their traditional counterparts in solving such optimization problems in different domains. Toward this end, this article proposes a quantum inspired green communication framework for Energy Balancing in sensor enabled IoT systems (Q-EBIoT). First, an energy optimization model for sensor enabled IoT environments is presented, where energy consumption is derived as cost of the energy-oriented paths. Second, a quantum computing oriented solution is developed for the optimization problem focusing on energy centric solution representation, measurement, and rotation angle. The proposed solution is implemented to evaluate the comparative performance with the state-of-the-art techniques. The evaluation demonstrates the benefit of the proposed framework in terms of various energy-related metrics for sensor enabled IoT environments.

Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

Quantum inspired genetic algorithm for multi-hop energy balanced unequal clustering in wireless sensor networks

DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of these have solely dwelt on the single objective of maximizing the sum of the overlaps between adjacent fragments in order to optimize the fragment layout. This paper aims to formulate this FAP as a bi-objective optimization problem, with the two objectives being the maximization of the overlap between the adjacent fragments and the minimization of the overlap between the distant fragments. Moreover, since there is greater desirability for having lesser number of contigs, FAP becomes a tri-objective optimization problem where the minimization of the number of contigs becomes the additional objective. These problems were solved using the multi-objective genetic algorithm NSGA-II. The experimental results show that the NSGA-II-based Bi-Objective Fragment Assembly Algorithm (BOFAA) and the Tri-Objective Fragment Assembly Algorithm (TOFAA) are able to produce better quality layouts than those generated by the GA-based Single Objective Fragment Assembly Algorithm (SOFAA). Further, the layouts produced by TOFAA are also comparatively better than those produced using BOFAA.

DNA Fragment Assembly Using Multi-Objective Genetic Algorithms

Clustering has been one of the most commonly used strategies for maximizing the lifetime of wireless sensor networks (WSNs). Clustering in WSNs is the process of grouping the sensors based on some criteria and optimal clustering in WSNs is known to be a NP-Hard problem. Evolutionary algorithms (e.g. genetic algorithm) have been extensively utilized for addressing this problem. In this paper, energy efficient clustering problem has been dealt with using a relatively new meta-heuristic technique known as quantum inspired genetic algorithm. The simulation results and analysis clearly indicate that the proposed approach outperforms genetic algorithm based clustering technique and leads to significant increase in network lifetime.

Manisha Rathee

Papers

Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks

Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments

Quantum inspired genetic algorithm for multi-hop energy balanced unequal clustering in wireless sensor networks

DNA Fragment Assembly Using Multi-Objective Genetic Algorithms

Quantum inspired genetic algorithm for energy efficient clustering in wireless sensor networks