Proposing a New Framework for Optimizing Energy Consumption in Sensor Nodes Used in the Internet of Things

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Azamossadat Nourbakhsh, Mohammad Ordouei, Bahareh Jalali


This  paper  presents  a  comparative  analysis  of  evolutionary  algorithms,  including genetic  algorithms,  particle  swarm  optimization,  cuckoo  algorithm,  and  the  Harris  Hawk Optimization algorithm, for optimizing vehicle routing in smart cities. The study evaluates the performance of these algorithms in minimizing costs and maximizing efficiency in the context of  providing  services  to  requesters.  Results  indicate  the  effectiveness  of  the  Harris  Hawk Optimization algorithm compared to other approaches, suggesting its suitability for real-world applications  in  smart  city  environments.  Future  directions  for  research  in  this  area  are  also discussed.Keywords: Evolutionary algorithms, Vehicle routing, Smart cities, Optimization, Comparative analysis1.IntroductionSmart transportation has emerged as an indispensable necessity and solution in today's traffic-congested  cities.  Urbanization  presents  significant  challenges,  including  traffic  congestion, pollution, and inefficiencies in transportation systems. In response to these challenges, smart cities have been developed, leveraging Information and Communication Technology (ICT) to promote sustainable development approaches[1].Afundamental  aspect  of  smart  cities  is  the  implementation  of  intelligent  transportation systems,  which  optimize  vehicle  routing  to  minimize  congestion,  reduce  travel  time,  and enhance overall efficiency. Evolutionary algorithms have gained prominence as effective tools for addressing complex optimization problems in smart transportation systems[2].This  research  focuses  on  improving  vehicle  routing  in  smart  cities  using  evolutionary algorithms,  with  a  particular  emphasis  on  the  Hybrid  Harris's  Hawks  Optimization  (HHO) algorithm[3]. The HHO  algorithm is a  gradient-independent optimization technique inspired by  the  cooperative  behavior  and  agile  pursuit  of  Harris's  Hawks  in  nature,  known  as  the "surprise pounce."

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