DABFS: A Robust Routing Protocol for Warning Messages Dissemination in VANETs

Vehicular ad hoc networks play a pivotal role in the enrichment of transportation systems by making them intelligent and capable of avoiding road accidents. For transmission of warning messages, direction-based Greedy protocols select the next hop based on the current location of relay nodes toward the destination node, which is an efficient approach for uni-directional traffic. However, such protocols experience performance degradation by neglecting the movement directions of nodes in bi-directional traffic where topological changes occur dynamically. This paper pioneers the use of movement direction and relative positions of source and destination nodes to cater to the dynamic nature of bi-directional highway environments for efficient and robust routing of warning messages. A novel routing protocol, namely, Direction Aware Best Forwarder Selection (DABFS), is presented in this paper. DABFS takes into account directions and relative positions of nodes, besides the distance parameter, to determine a node’s movement direction using Hamming distance and forwards warning messages through neighbor and best route discovery. Analytical and simulation results demonstrate that DABFS offers improved throughput and reduced packet loss rate and end-to-end delay, as compared with eminent routing protocols

Spectrum Efficiency in CRNs using Hybrid Dynamic Channel Reservation and Enhanced Dynamic Spectrum Access

Blocking of new arriving services and dropping of ongoing services are inherent problems in Cognitive Radio Networks (CRNs), which need to be addressed to enhance spectrum efficiency. In particular, Secondary Users (SUs) undergo service degradation in the face of Primary Users (PUs)’ arrivals. In this paper, we present a scheme called Efficient Spectrum Utilization (ESU) that reduces the dropping and blocking probabilities of existing and new services, respectively, to make efficient use of the available spectrum. The scheme divides the available spectrum into reserved and non-reserved bands. The reserved band is dynamically allocated a number of channels from the non-reserved band in order to accommodate those services which face interruptions while operating in the non-reserved band. The scheme renders dynamic access to the available spectrum and facilitates priority-based channel allocation and termination. SUs are divided into low and high priority levels depending on their Quality of Service (QoS) requirements. SUs with low priority level are granted direct access to both the bands to enhance channel utilization. SUs operating in the reserved band with high priority levels are granted uninterruptible status to ensure a certain level of service provisioning to SUs. The proposed ESU scheme is modeled using Continuous Time Markov Chain (CTMC) and mathematical expressions are derived for several QoS parameters. Performance of the proposed scheme is evaluated under various network conditions. Results demonstrate that ESU reasonably improves spectrum efficiency under channel failure in CRNs

A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs

Performance Analysis and Beamforming Design of a Secure Cooperative MISO-NOMA Network.

This paper studies the cell-edge user's performance of a secure multiple-input single-output non-orthogonal multiple-access (MISO-NOMA) system under the Rayleigh fading channel in the presence of an eavesdropper. We suppose a worst-case scenario that an eavesdropper has ideal user detection ability. In particular, we suggest an optimization-based beamforming scheme with MISO-NOMA to improve the security and outage probability of a cell-edge user while maintaining the quality of service of the near-user and degrading the performance of the eavesdropper. To this end, power allocation coefficients are adjusted with the help of target data rates of both the users by utilizing a simultaneous wireless information and power transfer with time switching/power splitting protocol, where the near-user is used to forward the information to cell-edge user. The analytical results demonstrate that our beamformer analysis can achieve reduced outage probability of cell-edge user in the presence of the eavesdropper. Moreover, the provided simulation results validate our theoretical analysis and show that our approach improves the overall performance of a two-user cooperative MISO-NOMA system

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively

Effect of gamma radiation on different stages of Indian meal moth Plodia interpunctella Hübner (Lepidoptera: Pyralidae)

Indian meal moth Plodia interpunctella Hübner is one of the most important stored products pests in the world. In this research, the effect of gamma irradiation was studied on different developmental stages of this pest and the doses required to prevent each of these developmental stages was investigated. From the results, required dose to prevent larval emergence from irradiated 1 to 24 h eggs was 400 Gray (Gy), and 400 Gy was required to prevent pupae from 15 days old larvae. Also, the dose of radiation required to prevent adult emergence from irradiated 5 days old pupa was 650 Gy. According to the results, dose of 650 Gy is adequate to control all immature stages of this pest. In addition, the effect of gamma ray was studied on developmental stage period of each irradiated existence stage till adult eclosion. The results revealed that there was a dose-dependent increase in the developmental periods, and the growth index of the adults was significantly decreased with increasing dose of radiation administered to the eggs, larvae and pupae too. It is concluded that irradiation can be used as a safe method to control stored pests.Key words: Gamma irradiation, prevention dose, developmental period, growth index, Plodia interpunctella

PDMAC: A Priority-based Enhanced TDMA Protocol for Warning Message Dissemination in VANETs

Vehicular Ad hoc Networks (VANETs) are the key enabling technology for intelligent transportation systems. Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) is the de facto media access standard for inter-vehicular communications, but its performance degrades in high-density networks. Time-Division Multiple Access (TDMA)-based protocols fill this gap to a certain extent, but encounter inefficient clock synchronization and lack of prioritized message delivery. To this end, we propose a Priority-based Direction-aware Media Access Control (PDMAC) as a novel protocol for intra-cluster and inter-cluster clock synchronization. Furthermore, PDMAC pioneers a three-tier priority assignment technique to enhance warning messages delivery by taking into account the direction component, message type, and severity level on each tier. Analytical and simulation results validate the improved performance of PDMAC in terms of clock synchronization, channel utilization, message loss rate, end-to-end delays and network throughput, as compared with eminent VANET MAC protocols

A secure remote user authentication scheme for 6LoWPAN-based Internet of Things.

One of the significant challenges in the Internet of Things (IoT) is the provisioning of guaranteed security and privacy, considering the fact that IoT devices are resource-limited. Oftentimes, in IoT applications, remote users need to obtain real-time data, with guaranteed security and privacy, from resource-limited network nodes through the public Internet. For this purpose, the users need to establish a secure link with the network nodes. Though the IPv6 over low-power wireless personal area networks (6LoWPAN) adaptation layer standard offers IPv6 compatibility for resource-limited wireless networks, the fundamental 6LoWPAN structure ignores security and privacy characteristics. Thus, there is a pressing need to design a resource-efficient authenticated key exchange (AKE) scheme for ensuring secure communication in 6LoWPAN-based resource-limited networks. This paper proposes a resource-efficient secure remote user authentication scheme for 6LoWPAN-based IoT networks, called SRUA-IoT. SRUA-IoT achieves the authentication of remote users and enables the users and network entities to establish private session keys between themselves for indecipherable communication. To this end, SRUA-IoT uses a secure hash algorithm, exclusive-OR operation, and symmetric encryption primitive. We prove through informal security analysis that SRUA-IoT is secured against a variety of malicious attacks. We also prove the security strength of SRUA-IoT through formal security analysis conducted by employing the random oracle model. Additionally, we prove through Scyther-based validation that SRUA-IoT is resilient against various attacks. Likewise, we demonstrate that SRUA-IoT reduces the computational cost of the nodes and communication overheads of the network

Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes

Temperature Dependent Characteristics of Activated Carbons from Walnut Shells for Improved Supercapacitor Performance

Activated carbons (ACs) have been prepared from chemical treatment of walnut shells (WS) precursor at various temperatures (400‒800 °C) by using phosphoric acid (H3PO4) as activating agent. Influence of activation temperature on the porosity development and capacitive properties of resulting carbons was investigated. Thermal post-treatment of carbons previously activated at moderate temperature, e.g. 400 °C allowed further structural and porosity modification. Then, these carbons were investigated by scanning electron microscopy, Raman spectroscopy, energydispersive X-ray spectroscopy, electrochemical techniques and low temperature nitrogen adsorption exhibiting high BET specific surface area of approximately 2100 m2 g-1 and a total pore volume up to 1.3 cm3 g-1. Carbon material obtained through activation by H3PO4 at 400 °C and post-treated at 800 °C was used to make electrodes which were implemented to realize AC/AC capacitor using 1 mol L-1 Li2SO4. The electrochemical capacitor demonstrated high capacitance of 123 F g-1 per mass of one electrode, reduced cell resistance and stable capacitance for 5000 galvanostatic charge/discharge cycles at 1.0 A g-1