225 research outputs found
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Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments
[EN] 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.Kumar, S.; Kaiwartya, O.; Rathee, M.; Kumar, N.; Lloret, J. (2020). Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments. IEEE Systems Journal. 14(4):4663-4673. https://doi.org/10.1109/JSYST.2020.29758234663467314
Efficient and optimal routing using ant colony optimization mechanism for wireless sensor networks
Recently more number of routing protocols is discovered for better data routing in Wireless Sensor Network (WSN). However link failures exist in the network due to appearance of low energy nodes, low link gap connectivity while routing, etc. To compute low complexity routes and to minimize the energy consumption a nature bio inspired algorithm Ant Colony Optimization (ACO) mechanism is applied in the sensor networks. An Efficient and Optimal Routing using ACO is proposed. The premium route is determined with sub-premier nodes having high link-gap connectivity factor. The best premier nodes are selected from the sub-premier nodes on basis of bandwidth integrity and eternal energy factors for determining the premium route. The proposed work is validated by comparing the results of other existing techniques. The performance metrics proves that the proposed mechanism exhibits better throughput and delivery rate with low loss rate
A survey of network lifetime maximization techniques in wireless sensor networks
Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri
SWARM INTELLIGENCE BASED RELIABLE AND ENERGY BALANCE ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORK
Energy is an extremely crucial resource for Wireless Sensor Networks (WSNs). Many routing techniques have been proposed for finding the minimum energy routing paths with a view to extend the network lifetime. However, this might lead to unbalanced distribution of energy among sensor nodes resulting in, energy hole problem. Therefore, designing energy-balanced routing technique is a challenge area of research in WSN. Moreover, dynamic and harsh environments pose great challenges in the reliability of WSN. To achieve reliable wireless communication within WSN, it is essential to have reliable routing protocol. Furthermore, due to the limited memory resources of sensor nodes, full utilization of such resources with less buffer overflow remains as a one of main consideration when designing a routing protocol for WSN. Consequently, this paper proposes a routing scheme that uses SWARM intelligence to achieve both minimum energy consumption and balanced energy consumption among sensor nodes for WSN lifetime extension. In addition, data reliability is considered in our model where, the sensed data can reach the sink node in a more reliable way. Finally, buffer space is considered to reduce the packet loss and energy consumption due to the retransmission of the same packets. Through simulation, the performance of proposed algorithm is compared with the previous work such as EBRP, ACO, TADR, SEB, and CLR-Routing
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Optimising routing and trustworthiness of ad hoc networks using swarm intelligence
This thesis was submitted for the degree of Doctor of Philsophy and awarded by Brunel UniversityThis thesis proposes different approaches to address routing and security of MANETs using swarm technology. The mobility and infrastructure-less of MANET as well as nodes misbehavior compose great challenges to routing and security protocols of such a network. The first approach addresses the problem of channel assignment in multichannel ad hoc networks with limited number of interfaces, where stable route are more preferred to be selected. The channel selection is based on link quality between the nodes. Geographical information is used with mapping algorithm in order to estimate and predict the links’ quality and routes life time, which is combined with Ant Colony Optimization (ACO) algorithm to find most stable route with high data rate. As a result, a better utilization of the channels is performed where the throughput increased up to 74% over ASAR protocol. A new smart data packet routing protocol is developed based on the River Formation Dynamics (RFD) algorithm. The RFD algorithm is a subset of swarm intelligence which mimics how rivers are created in nature. The protocol is a distributed swarm learning approach where data packets are smart enough to guide themselves through best available route in the network. The learning information is distributed throughout the nodes of the network. This information can be used and updated by successive data packets in order to maintain and find better routes. Data packets act like swarm agents (drops) where they carry their path information and update routing information without the need for backward agents. These data packets modify the routing information based on different network metrics. As a result, data packet can guide themselves through better routes.
In the second approach, a hybrid ACO and RFD smart data packet routing protocol is developed where the protocol tries to find shortest path that is less congested to the destination. Simulation results show throughput improvement by 30% over AODV protocol and 13% over AntHocNet. Both delay and jitter have been improved more than 96% over AODV protocol. In order to overcome the problem of source routing introduced due to the use of the ACO algorithm, a solely RFD based distance vector protocol has been developed as a third approach. Moreover, the protocol separates reactive learned information from proactive learned information to add more reliability to data routing. To minimize the power consumption introduced due to the hybrid nature of the RFD routing protocol, a forth approach has been developed. This protocol tackles the problem of power consumption and adds packets delivery power minimization to the protocol based on RFD algorithm.
Finally, a security model based on reputation and trust is added to the smart data packet protocol in order to detect misbehaving nodes. A trust system has been built based on the privilege offered by the RFD algorithm, where drops are always moving from higher altitude to lower one. Moreover, the distributed and undefined nature of the ad hoc network forces the nodes to obligate to cooperative behaviour in order not to be exposed. This system can easily and quickly detect misbehaving nodes according to altitude difference between active intermediate nodes
Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization
Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman\u27s test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy
Applications of river formation dynamics
River formation dynamics is a metaheuristic where solutions are constructed by iteratively modifying the values associated to the nodes of a graph. Its gradient orientation provides interesting features such as the fast reinforcement of new shortcuts, the natural avoidance of cycles, and the focused elimination of blind alleys. Since the method was firstly proposed in 2007, several research groups have applied it to a wide variety of application domains, such as telecommunications, software testing, industrial manufacturing processes, or navigation. In this paper we review the main works of the last decade where the river formation dynamics metaheuristic has been applied to solve optimization problems
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