Network lifetime maximization has received continuous attention as green computing in wireless sensor networks. Recently, controlled mobility-based green computing has witnessed significant attention from academia and industrial research labs. It is due to the growing number of sensor-based services in mobility friendly nonhostile environments in our daily life. The intelligent mobility-aided repositioning of sensors is significantly challenging considering the critical constraints including irregular power depletion, static normal sensors, the correlation between sensor position, and coverage and connectivity. In this context, this paper proposes a network lifetime maximization framework based on balanced tree node switching. Specifically, a balanced tree-based network model for wireless sensor networks is designed focusing on energy consumption of sensor nodes in tree-based networks. The problem of lifetime maximization in tree-based network is identified considering energy loss rate, path load, and balancing factor. Two node-shifting algorithms are developed, namely, energy-based shifting and load-based shifting for balancing tree-based network in terms of energy. Analytical and simulation experimentbased comparative performance evaluation attests the benefit of the proposed framework as compared to the state-of-the-art techniques considering a number of energy-oriented metrics for wireless networks.Focusing on efficient and balanced energy consumption for uniform energy depletion among sensors, 2,4 various green computing techniques have been suggested. It includes duty cycle management, data aggregation, and controlled mobility-based lifetime maximization for green computing in WSNs. 5-9 One of the major constraints in duty cycle based techniques is the overhead involved in constructing broadcasting sets for assigning duty to the complete network. 10-14 It creates major network overhead and reduces actual data transmission capability of the network. Most of the data aggregation techniques are based on clustering approach, where networks are divided into manageable clusters to balance energy consumption in different areas of the networks. One of the major problem of the clusteringbased green computing is the frequent require of cluster head selection and updating. 11,[15][16][17][18][19] Recently, controlled mobility-based green computing in WSNs has witnessed considerable attention because of the growing significance of sensor technology in mobility-enabled nonhostile environment. In the mobility-based green computing, some designated mobile sensors intelligently change the network topology by repositioning their locations for balancing energy consumption among sensors. The intelligent repositioning is significantly challenging considering the 3 critical constraints of the network and sensor specifications. [20][21][22][23] The constraints include uneven power depletion, static nature of majority of normal sensors, and strong correlation between sensor position, and coverage and connectivity. In literature o...

Heterogeneous vehicular communication on the Internet of connected vehicle (IoV) environment is an emerging research theme toward achieving smart transportation. It is an evolution of the existing vehicular ad hoc network architecture due to the increasingly heterogeneous nature of the various existing networks in road traffic environments that need to be integrated. The existing literature on vehicular communication is lacking in the area of network optimization for heterogeneous network environments. In this context, this paper proposes a heterogeneous network model for IoV and service-oriented network optimization. The network model focuses on three key networking entities: vehicular cloud, heterogeneous communication, and smart use cases as clients. Most traffic-related data–oriented computations are performed at cloud servers for making intelligent decisions. The connection component enables handoff-centric network communication in heterogeneous vehicular environments. The use-case-oriented smart traffic services are implemented as clients for the network model. The model is tested for various service-oriented metrics in heterogeneous vehicular communication environments with the aim of affirming several service benefits. Future challenges and issues in heterogeneous IoV environments are also highlighted.

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