Key Perspectives in Power Aware Ad-hoc Internet of Things with Advanced Networks and Real Time Scenarios

Smart gadgets with integrated power optimization segments are the key perspectives that use Internet of Things (IoT) enabled technology to promote lifestyle advancements. It has an influence on a number of sectors in academia and/or business thanks to its strong integration with the current Cloud architecture. Recently, the Internet of Things has been acknowledged as a disruptive technology for the aerial ad hoc network. IoT may be thought of as a network inside a network. IoT-based networks rely heavily on the so-called self-organizing capability working in a dispersed manner in ad hoc networks, with users travelling at speeds ranging from walking pace to automobile, rail, or airline speed. IoT applications that assist logistics and the administration of ad hoc networks may be found in a broad variety. Utility companies are under pressure now to produce ever more enormous amounts of electricity. In megacities, there is an exponential rise in the number of people and energy users. Thus, the need for energy conservation is growing significantly on a global scale. The best way to optimise the rising energy demands and consumptions is as a consequence of the development of energy-monitoring systems. These solutions can cut current utilisation levels, stop energy waste, and make better use of our resources

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Generalized inattentional blindness from a Global Workspace perspective

We apply Baars' Global Workspace model of consciousness to inattentional blindness, using the groupoid network method of Stewart et al. to explore modular structures defined by information measures associated with cognitive process. Internal cross-talk breaks the fundamental groupoid symmetry, and, if sufficiently strong, creates, in a highly punctuated manner, a linked, shifting, giant component which instantiates the global workspace of consciousness. Embedding, exterior, information sources act as an external field which breaks the groupoid symmetry in a somewhat different manner, definng the slowly-acting contexts of Baars' theory and providing topological constraints on the manifestations of consciousness. This analysis significantly extends recent mathematical treatments of the global workspace, and identifies a shifting, topologically-determined syntactical and grammatical 'bottleneck' as a tunable rate distortion manifold which constrains what sensory or other signals can be brought to conscious attention, typically in a punctuated manner. Sensations outside the limits of that filter's syntactic 'bandpass' have lower probability of detection, regardless of their structure, accounting for generalized forms of inattentional blindness

Internet of Things in Sustainable Energy Systems

Our planet has abundant renewable and conventional energy resources but technological capability and capacity gaps coupled with water-energy needs limit the benefits of these resources to citizens. Through IoT technology solutions and state-of-the-art IoT sensing and communications approaches, the sustainable energy-related research and innovation can bring a revolution in this area. Moreover, by the leveraging current infrastructure, including renewable energy technologies, microgrids, and power-to-gas (P2G) hydrogen systems, the Internet of Things in sustainable energy systems can address challenges in energy security to the community, with a minimal trade-off to environment and culture. In this chapter, the IoT in sustainable energy systems approaches, methodologies, scenarios, and tools is presented with a detailed discussion of different sensing and communications techniques. This IoT approach in energy systems is envisioned to enhance the bidirectional interchange of network services in grid by using Internet of Things in grid that will result in enhanced system resilience, reliable data flow, and connectivity optimization. Moreover, the sustainable energy IoT research challenges and innovation opportunities are also discussed to address the complex energy needs of our community and promote a strong energy sector economy

Paper plate machine

In recent years natural fibers appear to be the outstanding materials which come as the viable and abundant substitute for the expensive and nonrenewable synthetic fiber. Natural fibers like sugar cane, banana, , oil palm, kenaf and coir has been used as reinforcement in thermoplastic composite for applications in consumer goods, furniture, low cost housing and civil structures. Pineapple leaf fiber (PALF) is one of them that have also good potential as reinforcement in thermoplastic composite. It is the objective of the current research to characterize PALF and to investigate the effect of fiber treatment on the mechanical properties of PALF reinforced polypropylene (PP) composite. Figure 6.1 show the example of pineapple used