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Energy is a precious resource in the sensors-enabled Internet of Things (IoT). Unequal load on sensors deplete their energy quickly, which may interrupt the operations in the network. Further, a single artificial intelligence technique is not enough to solve the problem of load balancing and minimize energy consumption, because of the integration of ubiquitous smart-sensors-enabled IoT. In this paper, we present an adaptive neuro fuzzy clustering algorithm (ANFCA) to balance the load evenly among sensors. We synthesized fuzzy logic and a neural network to counterbalance the selection of the optimal number of cluster heads and even distribution of load among the sensors. We developed fuzzy rules, sets, and membership functions of an adaptive neuro fuzzy inference system to decide whether a sensor can play the role of a cluster head based on the parameters of residual energy, node distance to the base station, and node density. The proposed ANFCA outperformed the state-of-the-art algorithms in terms of node death rate percentage, number of remaining functioning nodes, average energy consumption, and standard deviation of residual energy.

Geocasting in vehicular communication has witnessed significant attention due to the benefits of location oriented information dissemination in vehicular traffic environments. Various measures have been applied to enhance geocasting performance including dynamic relay area selection, junction nodes incorporation, caching integration, and geospatial distribution of nodes. However, the literature lacks towards geocasting under malicious relay vehicles leading to cybersecurity concern in vehicular traffic environments. In this context, this paper presents Cybersecurity Measures for Geocasting in Vehicular traffic environments (CMGV) focusing on security oriented vehicular connectivity. Specifically, a vehicular intrusion prevention technique is developed to measure the connectivity between the cache agent and cache user vehicles. The connectivity between static transport vehicles and cache agent/cache user is measured via vehicular intrusion detection approach. The performance of the proposed vehicular cybersecurity measure is evaluated in realistic traffic environments. The comparative performance evaluation attests the benefits of security oriented geocasting in vehicular traffic environments. Index Terms-Geocasting, Vehicular ad-hoc networks, Vehicle cybersecurity, Caching I. INTRODUCTION OCATION oriented decision making in driverless cars is one of the finest examples of the significance of location oriented communication in vehicular cyber physical systems environments (see Fig. 1) [1, 2, 3]. The location-oriented services in vehicular environments is continuously growing staring from navigation to real time traffic prediction. It includes smart use cases of intelligent transport system such as safety and efficiency oriented cooperative vehicular communication, and sensor oriented emergency response for driver assistance [4, 5]. Recently, location oriented vehicular communication also known as geocasting has witnessed significant attention considering its applicability in the wide range of ITS applications [6].

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Edge computing has received significant attention from academia and industries and has emerged as a promising solution for enhancing the information processing capability at the edge for next generation 6G networks. The technical design of 6G edge networks in terms of offloading the computationally extensive task is very critical because of the overgrowth in data volume primarily due to the explosion of smart IoT devices, and the ever-reducing size of these energyconstrained devices in IoT systems. Toward harnessing the benefits of deep recurrent neural network based on Long Short Term Memory (LSTM) in the design of next-generation edge networks, this paper presents a framework DECENT-Deep learning Enabled green Computation for Edge centric Next generation 6G neTworks. The data offloading problem is modeled as a Markov decision process considering joint optimization of energy consumption, computation latency, and offloading rate for network utility in 6G environment. The algorithm learns faster from previous longterm offloading experiences and solves the optimization problem with better convergence speed. Simulation results of the proposed framework DECENT shows that it maximizes the network utility by overcoming the challenges as compared to the state-of-the-art techniques.