Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resourcelimited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this paper, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to longterm constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.

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Energy consumption is one of the constraints in Wireless Sensor Networks (WSNs). The routing protocols are the hot areas to address quality-of-service (QoS) related issues viz. Energy consumption, network lifetime, network scalability and packet overhead. The key issue in WSN is that these networks suffer from the packet overhead, which is the root cause of more energy consumption and degrade the QoS in sensor networks. In WSN, there are several routing protocols which are used to enhance the performance of the network. Out of those protocols, Dynamic Source Routing (DSR) protocol is more suitable in terms of small energy density, but sometimes when the mode of a node changes from active to sleep, the efficiency decreases as the data packets needs to wait at the initial point where the packet has been sent and this increases the waiting time and end to end delay of the packets which leads to increase in energy consumption. Our problem is to identify the dead nodes and to choose another suitable path so that the data transmission becomes smoother and less energy gets conserved. In order to resolve these issues, we propose directional transmission based energy aware routing protocol named as PDORP. The proposed protocol PDORP has the characteristics of both Power Efficient Gathering Sensor Information System (PEGASIS) and DSR routing protocols.In addition, hybridization of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) is applied to proposed routing protocol to identify energy efficient optimal paths. The performance analysis, comparison through a hybridization approach of the proposed routing protocol gives better result comprising less bit error rate, less delay, less energy consumption and better throughput which leads to better QoS and prolong the lifetime of the network. Moreover, the Computation Model is adopted to evaluate and compare the performance of the both routing protocols using soft computing techniques.

Recently, Named Data Networking (NDN) has been proposed as a promising architecture for future Internet technologies. NDN is an extension to the Content Centric Network (CCN) and is expected to support various applications in Vehicular communications (VNDN). VNDN, basically relies on naming the content rather than using end-to-end device names. In VNDN, a vehicle broadcasts an "Interest" packet for the required "content" regardless of end-to-end connectivity with servers or other vehicles and known as a "consumer". In response, a vehicle with the content, replies to the Interest packet with a "Data" packet and named as a "provider". However, the simple VNDN architecture faces several challenges such as consumer/provider mobility, Interest/Data packet(s) forwarding and so on. In VNDN, mostly the Data is sent along the reverse path of the related Interest packet. However, there is no extensive simulated reference available in the literature to support this argument. In this paper, therefore, we first analyze the propagation behavior of Interest and Data packets in the VANET environment through extensive simulations. Second, we propose "CODIE" scheme to control the Data flooding/broadcast storm in the naïve VNDN. The main idea is to allow the consumer vehicle to start hop counter in Interest packet. Upon receiving that Interest by any potential provider, a DDL value stores the number of hops, a data packet needs to travel back. Simulation results show that CODIE forwards less Copies of Data Packets Processed (CDPP) while achieving similar Interest Satisfaction Rate (ISR) as compared to the Naïve VNDN. In addition, we also found that CODIE also minimizes the overall Interest Satisfaction Delay (ISD), respectively.