Communication Is Essential To Provide An Intelligent Service To Connected Cars. For Operational Services, Connected Vehicles In VANET (Vehicle Ad Hoc Networks) Regularly Try To Transfer Large Amounts Of Data For Vehicular Safety. Similarly, V2x (Vehicle-to-Everything) Communication Includes Vehicles Transferring Information With Each Other And With Infrastructure I.e., Vehicle-to-Infrastructure (V2I) And Vehicle-to-Vehicle (V2V) Are Proven To Increase Traffic Safety And Security As Well As To Enhance The Efficiency Of Intelligent Transportation System (ITS). Vehicular Connectivity Is Provided Using Short-range Technologies Such As The IEEE 802.110 Standard Or Cellular Approaches, Such As The 5G Network. In This Article, We Consider Combining These Technologies In A Cooperative Manner And Aiming At Exploiting Jointly Their Advantages. In This Cooperative Heterogeneous Network, The IEEE 802.11p Supports Safety-related Pilot Use Cases While The Provision Of Non-safety Related Pilot Use Cases Are Supported By The 5G Test Network.
Wireless Sensor Networks (WSNs) Are Among The Most Popular Wireless Technologies For Sensor Communication Purposes Nowadays. Usually, WSNs Are Developed For Specific Applications, Either Monitoring Purposes Or Tracking Purposes, For Indoor Or Outdoor Environments, Where Limited Battery Power Is A Main Challenge. To Overcome This Problem, Many Routing Protocols Have Been Proposed Through The Last Few Years. Nevertheless, The Extension Of The Network Lifetime In Consideration Of The Sensors Capacities Remains An Open Issue. In This Paper, To Achieve More Efficient And Reliable Protocols According To Current Application Scenarios, Two Well-known Energy Efficient Protocols, I.e., Low-Energy Adaptive Clustering Hierarchy (LEACH) And Energy–Efficient Sensor Routing (EESR), Are Redesigned Considering Neural Networks. Specifically, To Improve Results In Terms Of Energy Efficiency, A Levenberg–Marquardt Neural Network (LMNN) Is Integrated. Furthermore, In Order To Improve The Performance, A Sub-cluster LEACH-derived Protocol Is Also Proposed. Simulation Results Show That The Sub-LEACH With LMNN Outperformed Its Competitors In Energy Efficiency. In Addition, The End-to-end Delay Was Evaluated, And Sub-LEACH Protocol Proved To Be The Best Among Existing Strategies. Moreover, An Intrusion Detection System (IDS) Has Been Proposed For Anomaly Detection Based On The Support Vector Machine (SVM) Approach For Optimal Feature Selection. Results Showed 96.15% Accuracy—again Outperforming Existing IDS Models. Therefore, Satisfactory Results In Terms Of Energy Efficiency, End-to-end Delay And Anomaly Detection Analysis Were Attained.
In This Paper, We Propose An Energy-aware Routing Algorithm And A Control Overhead Reduction Technique For Prolonging The Network Lifetime Of Software-defined Multihop Wireless Sensor Networks (SDWSNs). This Is An Effort To Optimize The Energy Consumption Of WSNs That Provide Services To The Industrial Internet Of Things (IoT). A Centralized Controller Grants A Global View Of The Sensor Network By Introducing Extra Control Overhead In The Network, But This Leads To Extra Energy Costs. However, Our New Algorithm Takes Advantage Of This Global View And Balances The Network Energy By Selecting Paths With The Highest Remaining Energy Level Among Multiple Paths For Each Sensor Node. We Also Identify Key Functions Draining Energy From The SDWSN And Minimize Their Impact By Implementing A Data Packet Aggregation Function, And Minimizing The Control Overhead By Keeping Track Of The Sensor Nodes’ Routing Tables Using A Simple Checksum Function. We Show That The Proposed Approach Prolongs The Network Lifetime Of The WSN By 6.5% On Average Compared To The Standard Shortest-path Algorithm, And That The Control Overhead Is Reduced By Approximately 12% While Still Maintaining A Very High Packet Delivery Ratio.
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