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This paper investigates the reliability and security performance of the downlink non-orthogonal multiple access (NOMA) networks over Nakagami-m fading channels, where the base station (BS) aims to communicate with multi-antenna NOMA users in the presence of a multi-antenna eavesdropper. To be more practical, a detrimental factor at both transmitter and receiver is considered, namely in-phase and quadrature-phase imbalance (IQI). To further improve the reliability and security of the considered networks, the selection combining (SC) algorithm at the receiver is taken into account. More specifically, the exact analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived in closed-form. To obtain a better understanding of the influence for the IQI parameters on the system performance, the asymptotic behaviors for the outage probabilities (OPs) in the high signal-to-noise ratio (SNR) region are analyzed. Based on the asymptotic results, the diversity order of the considered system are obtained and discussed. The numerical results are presented to verify the validity of the theoretical analysis.

Drawing on the central themes of open innovation of the knowledge inflows and outflows for a firm’s innovative achievement, this research investigates the application of external knowledge (i.e. competitor intelligence) for product innovation. We posit that open-mindedness and inter-functional coordination mediate the relationship between competitor intelligence and product innovation. We further explore the joint moderating effect of the dynamic emerging market environment on the effects of open-mindedness and inter-functional coordination on product innovation. Survey data were collected from 284 executives from Chinese IT SMEs. Analysis methods of Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) are applied. Results showed that competitor intelligence has a positive and direct effect on product innovation, and their relationships can be further strengthened by the mediators—open-mindedness and inter-functional coordination. In testing the interaction with the dynamic emerging market, we found that the external environment interacts positively with open-mindedness, but negatively with inter-functional coordination on the effect on product innovation. This research enriches our understanding of external knowledge inflow for open innovation application. It contextualises the positive impact of being an open-minded and inter-functional coordinated on a firm’s product innovation. Managerial implications and future research avenues are discussed

Due to the rapidly growing sensor-enabled connected world around us, with the continuously decreasing size of sensors from smaller to tiny, energy efficiency in wireless sensor networks has drawn ample consideration in both academia as well as in industries’ R&D. The literature of energy efficiency in wireless sensor networks (WSNs) is focused on the three layers of wireless communication, namely the physical, Medium Access Control (MAC) and network layers. Physical layer-centric energy efficiency techniques have limited capabilities due to hardware designs and size considerations. Network layer-centric energy efficiency approaches have been constrained, in view of network dynamics and available network infrastructures. However, energy efficiency at the MAC layer requires a traffic cooperative transmission control. In this context, this paper presents a one-dimensional discrete-time Markov chain analytical model of the Timeout Medium Access Control (T-MAC) protocol. Specifically, an analytical model is derived for T-MAC focusing on an analysis of service delay, throughput, energy consumption and power efficiency under unsaturated traffic conditions. The service delay model calculates the average service delay using the adaptive sleep wakeup schedules. The component models include a queuing theory-based throughput analysis model, a cycle probability-based analytical model for computing the probabilities of a successful transmission, collision, and the idle state of a sensor, as well as an energy consumption model for the sensor’s life cycle. A fair performance assessment of the proposed T-MAC analytical model attests to the energy efficiency of the model when compared to that of state-of-the-art techniques, in terms of better power saving, a higher throughput and a lower energy consumption under various traffic loads.

In multi-hop wireless communication, a sensor node must consume its energy efficiently for relaying data packets. However, most of the IoT-devices are equipped with limited battery power and computing resources for wireless communication thus energy optimization becomes one of the major concerns in wireless sensors routing design. The wireless technologies usually use unlicensed frequency bands of 2.4 GHz to transmit the data. Due to the broadcasting medium, the wireless transmission interferes with the reception of surrounding radios. As a result, data transmission failure increases and thus low communication quality. Therefore, one of the best solutions of this problem is to select the hop distance node that has a few neighbour nodes to disseminate a packet until it reaches the ultimate receiver. The new routing selects the node that has few neighbouring nodes and thus less interference. In another word, the scheme finds a better load balancing and thus minimizes the probability of overload on a sensor node. It also introduces a new clustering algorithm around a single base station that could shorten the transmission distances. This approach periodically selects the cluster heads (CHs) according to its location from the final destination. Extensive simulation studies reveal that the proposed algorithm finds the best routing technique and clustering formation to forward the traffic and thereby minimizes the interference ratio. In addition, the proposed protocol achieves low energy consumption and longer network lifetime than other popular protocols.

Energy is a precious resource in sensors enabled Internet of Things (IoT). Unequal load on sensors deplete their energy quickly that may interrupt operations of the network. Further, single artificial intelligence technique is not to be enough to fulfill the problem of load balancing and minimize energy consumption because of integration of ubiquitous nature of smart sensors enabled IoT. In this paper, we present an adaptive neuro fuzzy clustering algorithm (ANFCA) to balance the load evenly among sensors. We synthesize fuzzy logic and neural network to counterbalance the selection of optimal number of cluster heads and evenly distribution of load among sensors. We develop fuzzy rules, sets, and membership functions of adaptive neuro fuzzy inference system to decide whether a sensor play the role of cluster head. The proposed ANFCA outperforms the state of the art algorithms in terms of node death percentage, number of alive nodes, average energy consumption, and standard deviation of residual energy.

Due to the scarce energy supplies of the internet of things (IoT) devices, it is necessary to design the architecture and operation of the network to optimize the power usage. Scheduling algorithm is an essential part of WSNs and IoT networks. Such algorithms allow to classify the queues and decide which process to run. Therefore, scheduling message at cluster head nodes has been suggested for this study. Long hop (LH) first algorithm is new unified scheduling technique that schedules high priority for data comes from far distances and accesses higher number of devices to be routed first to the target. The performance experience shows the proposed method compares with two existing studies, a popular scheduling algorithm is called first-come-first-serve (FCFS) and nearest job next (NJN) algorithms. The results and simulation have been shown that the proposed study has consumed less power and maximize throughput. It saves up to 56% power consumption and 62.50% in network throughput. In addition, it minimizes the packets delay and loss, transmission distance and thus extend the lifetime of the network.

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.

Cloud computing provides platform for pay per use services such as software (e.g., database, data processing, application servers, etc.), hardware (e.g., GPUs, CPUs, storage, etc.) and platforms (e.g., Linux, Microsoft Windows and Apple macOS). Previous cloud frameworks use fix policies that do not have the functionality to upgrade services on demand when the user do not receive services according to Service Level Agreement (SLA). Also, there was a lack of functionality to monitor external network and client device resources. This paper presents Quality of experience framework for Cloud computing (QoC) for monitoring the Quality of Experience (QoE) of the end user using video streaming services in the cloud computing environment. The management platform is used for administration purpose in QoC framework that provides facility to easily manage the cloud environment and provide services according to SLA via runtime policy change. The objective QoE/QoS section will automatically monitor the Quality of Service (QoS) data. It will also compare and analyze the subjective QoE submitted by the users and objective QoS data collected by agent based framework for accurate QoE prediction and proper management. The proposed QoC framework has new features of real time network monitoring, client device monitoring and allows changing policy in runtime environment which to our knowledge is currently not provided by existing frameworks.

Internet of things (IoT) is considered to revolutionize the way internet works and bring together the concepts such as machine to machine (M2M) communication, big data, artificial intelligence, etc. to work under a same umbrella such that cyber space and human (physical systems) are more intertwined and thus ubiquitous giving rise to cyber physical systems. This will involve billions of connections and smart products communicating with each other mostly without human intervention to achieve smart objectives. The idea of IoT has enticed significant research attentions since the massive connectivity bring varieties of challenges and obstacles including heterogeneity, scalability, security, big data, energy requirements, etc. The paper looks into providing a concise review of the concepts on IoT and applications describing the main components and features. Furthermore, open issues and challenges that need addressing by the research community and some potential solutions are discussed.

Recently, Internet of Things (IoT) have witnessed significant attention due to their potential of bringing massive number of interconnected heterogeneous sensor nodes to collect big data for harnessing knowledge thus enabling real time monitoring and control. These wireless sensors relay their packets on to the base station via multi-hop transmissions and is therefore dependent on the intermediate nodes. However, unbalanced data forward among intermediate nodes results in high-energy consumption and packet drops. Since, the network lifetime and link reliability are the most import factors for multi-hop environment this paper proposes a link quality-oriented route (LQOR) protocol for scalable IoT networks. The main goal of this scheme is to balance the load for any sensor by selecting the next hop such that it has less number of neighboring nodes. Specifically, an architecture for a typical IoT network is presented that uses LQOR and various metrics for the network, such as network lifetime, energy consumption, etc. are analyzed. The comparative performance evaluation corroborates the positive impact of the proposed algorithm on the entire path as compared to other techniques in the literature. We show that the proposed algorithm achieves less packets loss and minimizes the number of data re-transmissions. Thus, improving the overall system performance and elongating the network lifetime.

Cloud computing provides platform for pay per use services such as software (e.g., database, data processing, application servers, etc.), hardware (e.g., GPUs, CPUs, storage, etc.) and platforms (e.g., Linux, Microsoft Windows and Apple macOS). Previous cloud frameworks use fix policies that do not have the functionality to upgrade services on demand when the user do not receive services according to Service Level Agreement (SLA). Also, there was a lack of functionality to monitor external network and client device resources. This paper presents Quality of experience framework for Cloud computing (QoC) for monitoring the Quality of Experience (QoE) of the end user using video streaming services in the cloud computing environment. The management platform is used for administration purpose in QoC framework that provides facility to easily manage the cloud environment and provide services according to SLA via runtime policy change. The objective QoE/QoS section will automatically monitor the Quality of Service (QoS) data. It will also compare and analyze the subjective QoE submitted by the users and objective QoS data collected by agent based framework for accurate QoE prediction and proper management. The proposed QoC framework has new features of real time network monitoring, client device monitoring and allows changing policy in runtime environment which to our knowledge is currently not provided by existing frameworks.