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multiobjective routing protocol for wireless sensor network optimization using ant colony conveyance algorithm

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第6期34卷 e4270-e4270页

作者： Sasi, Swapna B. Santhosh, R. Karpagam Acad Higher Educ Fac Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

The widely deployed wireless sensor networks, incorporated with the capabilities of sensing, processing, and communicating, require more effective routing to maintain the balance between the performance parameters and to be prevented from the security attacks. The cryptographic methods used were effective but consumed more energy on the authentication, the encryption, and the decryption. So the routing addressing the security problems, with the reduced energy consumption, latency, and PDR losses, maximizing the network lifetime is proposed in the paper. This multiobjective routing problem of the sensor networks is handled using the ant colony routing techniques, taking into consideration the remaining energy of the nodes, the distance between the nodes, and the trust credentials of the nodes, to reduce the energy consumption and enhance the lifetime of the sensor networks. The further performance evaluation using the network simulator-2 reveals the efficiency and the quality of the service of proposed routing technique over existing techniques on the grounds of energy utilization, PDR, lifetime of sensor networks, and the security enhancement rate.

关键词： ant colony optimization energy consumption multiobjective routing network lifetime enhancements security attacks wireless sensor networks

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(Smart CPS) multiobjective dynamic routing with predefined stops for automated vehicles

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2019年第4-5期32卷 396-405页

作者： Horvath, Marton Tamas Tettamanti, Tamas Varga, Istvan Fac Transportat Engn & Vehicle Engn Dept Control Transportat & Vehicle Syst Budapest Hungary Univ Technol & Econ Budapest Hungary

Nowadays transportation represents an important field for CPS applications due to the rapid development of highly automated or autonomous vehicles, e.g. warehouse vehicles, airport people movers, public transport, etc. These vehicles are usually designed to circulate on fixed routes independently of the actual state of the network in order to fulfil a given criterion, such as traveling on the shortest path or including scheduled stops. This paper introduces a routing approach that allows automated vehicles to travel on different paths between given points, minimising the generalised cost of the route. Between fixed points, which can be either different storage points in a warehouse or simply public transport stops, possible routes are modelled as a continuously updated weighted directed graph. The weights represent relevant parameters of links, collected from surrounding sensors and monitoring systems of the network. Route optimisation is done by Yen's algorithm depending on the timetable: if the vehicle will reach the next stop on time, the alternative with the lowest generalised cost is chosen;else the fastest route is followed. The method is introduced specifically via the problem of traffic congestion on public transport paths, but can be generalised, e.g. any transport system within factories or warehouses.

关键词： multiobjective routing dynamic routing multiple edge-cost public transport

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multiobjective dynamic routing with predefined stops for automated vehicles

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International Journal of Computer Integrated Manufacturing 2019年第4-5期32卷 396-405页

ABSTRACTABSTRACTNowadays transportation represents an important field for CPS applications due to the rapid development of highly automated or autonomous vehicles, e.g. warehouse vehicles, airport people movers, public transport, etc. These vehicles are usually designed to circulate on fixed routes independently of the actual state of the network in order to fulfil a given criterion, such as traveling on the shortest path or including scheduled stops. This paper introduces a routing approach that allows automated vehicles to travel on different paths between given points, minimising the generalised cost of the route. Between fixed points, which can be either different storage points in a warehouse or simply public transport stops, possible routes are modelled as a continuously updated weighted directed graph. The weights represent relevant parameters of links, collected from surrounding sensors and monitoring systems of the network. Route optimisation is done by Yen’s algorithm depending on the timetable: if the vehicle will reach the next stop on time, the alternative with the lowest generalised cost is chosen; else the fastest route is followed. The method is introduced specifically via the problem of traffic congestion on public transport paths, but can be generalised, e.g. any transport system within factories or warehouses.

关键词： multiobjective routing dynamic routing multiple edge-cost public transport

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Chronological wild geese optimization algorithm for cluster head selection and routing in wireless sensor network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第2期38卷

作者： Mabunga, Zoren P. Dela Cruz, Jennifer C. Mapua Univ Sch Grad Studies Manila Philippines Southern Luzon State Univ Coll Engn Lucban Quezon Philippines Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

Wireless sensor networks (WSNs) consist of numerous sensor nodes with limited battery life, computational power, and network capabilities. These sensors are deployed in specific areas to monitor environmental physical parameters. Once the data are collected, it is processed and transmitted to a base station (BS) via designated routes. The processes of sensing and transmitting consume significant energy, leading to rapid depletion of node batteries and the occurrence of hot spot problems. Consequently, relying on a single route for data transmission can result in network overhead issues. Enhancing the energy efficiency of WSNs is a persistent challenge. To address this, improvements in processes, such as routing and clustering are necessary. Implementing dynamic cluster head (CH) selection is a key approach for optimal path selection and energy conservation. Accordingly, in this work, a novel multiobjective CH selection and routing method for providing energy-aware data transmission in WSN is presented. Here, CH selection is carried out using the proposed chronological wild geese optimization (CWGO) technique based on multiple constraints, such as delay, intercluster distance, intracluster distance, Link Life Time (LLT), and predicted energy. Further, the nodes' energy is determined by the deep recurrent neural network (DRNN). Then, the ideal path from the node to the BS is identified by the CWGO considering constraints, like predicted energy, delay, distance, and trust. Moreover, the proposed CWGO is examined considering metrics, like energy, trust, distance, and delay and is found to have attained superior values of 0.963 J, 0.700, 19.468 m, and 0.252 s, respectively. In this work, a hybrid optimization technique called CWGO is proposed by integrating the chronological concept in the wild geese algorithm (WGA). CH selection is accomplished by the CWGO on the basis of LLT, delay, predicted energy, intracluster distance and intercluster distance, and routing is perfo

关键词： chronological concept chronological wild geese optimization deep recurrent neural network multiobjective routing wild geese algorithm

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routing and emergency-response-team siting for high-level radioactive waste shipments

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 1998年第2期45卷 141-152页

作者： List, GF Turnquist, MA Rensselaer Polytech Inst Dept Civil Engn Troy NY 12180 USA Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA

Route choice and emergency-response-team siting are both important facets of any high-level radioactive-waste shipment campaign, These two sets of decisions are clearly related, and both involve multiple objectives, This paper describes a methodology for making these decisions jointly, in a logical and sequential fashion, and illustrates the technique using preliminary estimates of shipments to be made to the Waste Isolation Pilot Project facility.

关键词： decision support systems high-level radioactive waste transportation mixed-integer linear program multiobjective routing multiobjective siting

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Seeking the Pareto front for multiobjective spatial optimization problems

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2008年第5期22卷 507-526页

作者： Huang, B. Fery, P. Xue, L. Wang, Y. Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Hong Kong Peoples R China Natl Univ Singapore Dept Civil Engn Singapore 117548 Singapore Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing Peoples R China

Spatial optimization problems, such as route selection, usually involve multiple, conflicting objectives relevant to locations. An ideal approach to solving such multiobjective optimization problems (MOPs) is to find an evenly distributed set of Pareto-optimal alternatives, which is capable of representing the possible trade-off among different objectives. However, these MOPs are commonly solved by combining the multiple objectives into a parametric scalar objective, in the form of a weighted sum function. It has been found that this method fails to produce a set of well spread solutions by disregarding the concave part of the Pareto front. In order to overcome this ill-behaved nature, a novel adaptive approach has been proposed in this paper. This approach seeks to provide an unbiased approximation of the Pareto front by tuning the search direction in the objective space according to the largest unexplored region until a set of well-distributed solutions is reached. To validate the proposed methodology, a case study on multiobjective routing has been performed using the Singapore road network with the support of GIS. The experimental results confirm the effectiveness of the approach.

关键词： multiobjective optimization GIS pareto-front multiobjective routing

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