Search

Social Internet of Vehicles (SIoV) is an evolving vehicular networking framework integrating the next generation smart devices with vehicular communications. Green computing and communication under disruptive vehicular environment is one of the challenging tasks for enabling SIoV. In this context, green traffic data dissemination in SIoV environments is modelled as an NP-hard problem focusing on heterogeneous traffic data, transmission distance from next generation smart devices and probabilistic delay in transmissions due to disruptive vehicular environment. An adopted meta-heuristic solution namely Two-Way Particle Swarm Optimization (TWPSO) is developed for the green traffic data dissemination problem in SIoV considering software defined vehicular network architecture. Extensive simulation experiments were performed to assess the performance of TWPSO as compared to the state-of-the-art techniques. The critical analysis of the comparative results attest the green computing oriented benefits of TWPSO under real SIoV environments. [ABSTRACT FROM AUTHOR]

Vehicular Fog Computing (VFC) provides solutions to relieves overload cloudlet nodes, reduces service latency during peak times, and saves energy for battery-powered cloudlet nodes by offloading user tasks to a vehicle (vehicular node) by exploiting the under-utilized computation resources of nearby vehicular node. However, the wide deployment of VFC still confronts several critical challenges: lack of energy-latency tradeoff and efficient resource allocation mechanisms. In this paper, we address the challenges and provide an Energy-efficient dynamic Computation Offloading and resources allocation Scheme (ECOS) to minimize energy consumption and service latency. We first formulate the ECOS problem as a joint energy and latency cost minimization problem while satisfying vehicular node mobility and end-to-end latency deadline constraints. We then propose an ECOS scheme with three phases. In the first phase, we propose an overload cloudlet node detection policy based on resource utilization. In the second phase, we propose a computational offloading selection policy to select a task from an overloaded cloudlet node for offloading, which minimizes offloading cost and the risk of overload. Next, we propose a heuristic approach to solve the resource allocation problem between the vehicular node and selected user tasks for energy-latency tradeoff. Extensive simulations have been conducted under realistic highway and synthetic scenarios to examine the ECOS scheme's performance. In comparison, our proposed scheme outperforms the existing schemes in terms of energy-saving, service latency, and joint energy-latency cost. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) have witnessed significant attention from both industries and academia in research and development. This is due to the wide range of applications including scientific, commercial, military, and environmental. Considering the peculiar characteristics and harsh environments of UWSNs, reliable green communication among sensor nodes in the network is one of the major challenging tasks. In this context, this article proposes a localization-free shortest path reliable and energy-efficient pressure-based routing (SPRE-PBR) protocol for UWSNs. SPRE-PBR considers three parameters including residual energy, pressure, and link quality for selecting the next forwarding nodes. Moreover, SPRE-PBR is designed and developed to control path selection and reduce the unnecessary forwarding based on route cost calculation and optimal shortest path algorithm. The comprehensive performance evaluation attests the benefit of SPRE-PBR as compared with the state-of-the-art techniques considering underwater networking centric metrics. [ABSTRACT FROM AUTHOR]

One of the major bottlenecks toward realizing IoT systems is the energy constraint of sensors. Prolonging network lifetime is a fundamental issue for implementing IoT systems. The energy optimization problem, being NP-hard in nature for scalable networks, has been addressed in the literature using traditional metaheuristic techniques. Quantum inspired metaheuristics have shown better performance than their traditional counterparts in solving such optimization problems in different domains. Toward this end, this article proposes a quantum inspired green communication framework for Energy Balancing in sensor enabled IoT systems (Q-EBIoT). First, an energy optimization model for sensor enabled IoT environments is presented, where energy consumption is derived as cost of the energy-oriented paths. Second, a quantum computing oriented solution is developed for the optimization problem focusing on energy centric solution representation, measurement, and rotation angle. The proposed solution is implemented to evaluate the comparative performance with the state-of-the-art techniques. The evaluation demonstrates the benefit of the proposed framework in terms of various energy-related metrics for sensor enabled IoT environments. [ABSTRACT FROM AUTHOR]

Summary: 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 experiment–based 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. [ABSTRACT FROM AUTHOR]

Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues. [ABSTRACT FROM AUTHOR]

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network. [ABSTRACT FROM AUTHOR]

Postquantum cryptography for elevating security against attacks by quantum computers in the Internet of Everything (IoE) is still in its infancy. Most postquantum based cryptosystems have longer keys and signature sizes and require more computations that span several orders of magnitude in energy consumption and computation time, hence the sizes of the keys and signature are considered as another aspect of security by green design. To address these issues, the security solutions should migrate to the advanced and potent methods for protection against quantum attacks and offer energy efficient and faster cryptocomputations. In this context, a novel security framework Lightweight Postquantum ID-based Signature (LPQS) for secure communication in the IoE environment is presented. The proposed LPQS framework incorporates a supersingular isogeny curve to present a digital signature with small key sizes which is quantum-resistant. To reduce the size of the keys, compressed curves are used and the validation of the signature depends on the commutative property of the curves. The unforgeability of LPQS under an adaptively chosen message attack is proved. Security analysis and the experimental validation of LPQS are performed under a realistic software simulation environment to assess its lightweight performance considering embedded nodes. It is evident that the size of keys and the signature of LPQS is smaller than that of existing signature-based postquantum security techniques for IoE. It is robust in the postquantum environment and efficient in terms of energy and computations. [ABSTRACT FROM AUTHOR]

Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments. [ABSTRACT FROM AUTHOR]