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Summary: The Constrained Application Protocol (CoAP) is a lightweight web transfer protocol designed based on the REST architecture standardized by the Internet Engineering Task Force (IETF) to meet and accommodate the requirements of the constrained Internet of Things (IoT) environments. Managing congestion control in a resource‐constrained lossy network with a high bit error rate is a significantly challenging task that needs to be addressed. The primary congestion control mechanism defined by CoAP specification leverages on basic binary exponential backoff and often fails to utilize the network dynamics to the best of its traffic conditions. As a result, CoCoA has been introduced for better IoT resource utilization. In addition, CoCoA retransmission timeout (RTO) for network dynamics is based on constant coefficient values. The resource‐constrained nature of IoT networks poses new design challenges for congestion control mechanisms. In this paper, we propose a new particle swarm optimization (PSO)‐based congestion control approach called psoCoCoA as a variation of CoCoA. The psoCoCoA applies random and optimal parameter‐driven simulation to optimize default CoAP parameters and update the fitness and velocity positions to adapt to the traffic conditions. This process is performed for different traffic scenarios by varying the retransmission and max‐age values by using the optimization‐based algorithm. We carried out extensive simulations to validate the congestion control performance for CoAP with Observe, CoCoA, and psoCoCoA with different network topologies. The results indicate that psoCoCA outperforms or very similar to CoCoA and achieves better performance compared to CoAP with Observe under different network scenarios. [ABSTRACT FROM AUTHOR]

Parameter‐based adaptive threshold estimators (ATEs) are widely used for signal detection in cognitive radio (CR) systems. However, their performance deteriorates under dynamic spectra conditions owing to a lack of valid methods to accurately self‐tune the different parameters of such ATEs. In this article, we address this limitation by proposing a generalized system for self‐tuning the parameters of any ATE based only on the input signal measured per time. We adopt swarm and evolutionary‐based metaheuristic optimization techniques to effectively search for the optimal parameter values of any ATE. Our system controls the search process by applying the between‐class variance function adapted from Otsu's algorithm as the objective function. We tested the system using five different metaheuristic optimization algorithms (MOAs) to self‐tune two different ATEs, namely the recursive one‐sided hypothesis testing (ROHT) technique and the histogram partitioning algorithm under Rayleigh and Rician fading channels, as well as under different modulation schemes, including the 4‐quadrature amplitude modulation and 4‐phase shift keying schemes. Our findings suggest that our proposed system yields generally a small error rate irrespective of the MOA used. In addition, the ROHT‐cuckoo search optimization configuration yielded a reasonably high and low probability of detection and probability of false alarm, respectively, as a function of the signal‐to‐noise‐ratio of the input signal at a fast average processing time of 0.0699 seconds. We concluded that our system presents an effective mechanism that can be used to automatically tune the parameters of any ATE for useful signal detection in CR. [ABSTRACT FROM AUTHOR]

With the emerging applications of the Internet of Things (IoT), a congestion control mechanism becomes a critical phenomenon for efficient communication in networks of constrained devices. The Internet Engineering Task Force developed the constrained application protocol (CoAP) as a standard communication protocol that favors lightweight interoperability for accommodating resource‐constrained devices. However, the base CoAP specification congestion control is insensitive to various network conditions. Thus, differentiating the scenario of packet loss due to bit error rate and congestion, and identifying correct round trip time (RTT) of retransmitted message‐acknowledgement is quite essential to adapt the CoAP behavior based on the network status. In this paper, we present a context‐aware congestion control (CACC) approach for lightweight CoAP/user datagram protocol–based IoT traffic. The CACC proposes mechanisms that include retransmission timeout (RTO) estimator, retransmission count–based smoothed round‐trip‐time observation, lower bound RTO restriction approach, and aging concept. The proposed RTO estimators utilize the strong, weak, and failed RTT to identify exact network status and provide adaptive congestion control. The CACC incorporates the variable of retransmission count in request‐response interaction model to mitigate the negative variation in RTT due to the fluctuation in the IoT environment. Moreover, with lower bound RTO restriction approach, the unnecessary spurious retransmissions are avoided, and the aging mechanism limits the validity of the RTO value to improve the efficiency of the proposed scheme. The proposed model is validated against baseline CoAP and CoCoA+ using Contiki OS and the Cooja simulator. The results are impressive under different network topologies. [ABSTRACT FROM AUTHOR]

Eye tracking has a number of useful applications ranging from monitoring a vehicle driver for possible signs of fatigue, providing an interface to enable severely disabled people to communicate with others, to a number of medical applications. Most eye tracking applications require a non-intrusive way of tracking the eyes, making a camera-based approach a natural choice. However, although significant progress has been made in recent years, modern eye tracking systems still have not overcome a number of challenges including eye occlusions, variable ambient lighting conditions and inter-subject variability. This study describes the development of a robust real-time camera-based eye tracker, which is mainly suitable for indoor applications. The developed eye tracker relies on the so-called bright/dark pupil effect for both the eye detection and eye tracking phases. Furthermore, this study also aims to determine how strong the bright/dark pupil effect is among people from an African ethnical background, a feature that is very relevant to a country such as South Africa. [ABSTRACT FROM AUTHOR]

Software‐defined wireless sensor networking (SDWSN) is a new wireless networking paradigm formed by applying software‐defined networking to wireless sensor networks. Fragmentation‐based distributed control system offers an efficient way of distributing the SDWSN control services across the network. Fragmentation aims to bring the control services closer to the infrastructure network in order to reduce the propagation latency. It also aims to improve the scalability, reliability, and performance of the network. The fragmentation model is earmarked to play a huge role in stimulating participation of SDWSN in Internet of Things. To realize this, we optimize the model for deployment and integration as well as for operational efficiency. We consider two aspects: controller placement and controller reelection after failure. This paper discusses the controller placement techniques suitable for SDWSN and the controller replacement in a case of failure for SDWSN. The controller placement and the controller replacement mechanism were both evaluated and the results proved to be effective and efficient. [ABSTRACT FROM AUTHOR]