In this research we produced a mobile language learning game that is designed within a technical context. After conceptual analysis of the subject matter i.e. computer’s motherboard, the game was designed. The action within the game is consistent to the theme. There is a story, simplifying and exaggerating real life. Elements of control, feedback and sense of danger are incorporated into our game. By producing an engaging learning experience, vocabularies were learned incidentally. Deliberate vocabulary learning games were also added to our package to help students solve their common errors

Internet of Things (IoT) networks dependent on cloud services usually fail in supporting real-time applications as there is no response time guarantees. The fog computing paradigm has been used to alleviate this problem by executing tasks at the edge of the network, where it is possible to provide time bounds. One of the challenging topics in a fog-assisted architecture is to task placement on edge devices in order to obtain a good performance. The process of task mapping into computational devices is known as Service Placement Problem (SPP). In this paper, we present a heuristic algorithm to solve SPP, dubbed as clustering of fog devices and requirement-sensitive service first (SCATTER). We provide simulations using iFogSim toolkit and experimental evaluations using real hardware to verify the feasibility of the SCATTER algorithm by considering a smart home application. We compared the SCATTER with two existing works: edge-ward and cloud-only approaches, in terms of Quality of Service (QoS) metrics. Our experimental results have demonstrated that SCATTER approach has better performance compared with the edge-ward and cloud-only, 42.1% and 60.2% less application response times, 22% and 27.8% less network usage, 45% and 65.7% less average application loop delays, and 2.33% and 3.2% less energy consumption.

Unobtrusive personal data collection by wearable sensors and ambient monitoring has increased concerns about user privacy. Applying cryptography solutions to resource constraint wireless sensors as one of the privacy-preserving solutions demand addressing limited memory and energy resources. In this paper, we set up testbed experiments to evaluate the existing cryptographic algorithms for sensors, such as Skipjack and RC5, which are less secure compared to block cipher based on chaotic (BCC) on existing IEEE802.15.4 based SunSPOT sensors. We have proposed modified BCC (MBCC) algorithm, which uses chaos theory characteristics to achieve higher resistance against statistical and differential attacks while maintaining resource consumption. Our comparison observations show that MBCC outperforms BCC in both energy consumption and RAM usage and that both MBCC and BCC outperform RC5 and Skipjack in terms of security measures, such as entropy and characters frequency. Our comparison analysis of MBCC vs BCC suggests 13.44% lower RAM usage for encryption and decryption as well as 6.4 and 6.6 times reduced consumed time and energy for encrypting 32-bit data, respectively. Further analysis is reported for increasing the length of MBCC key, periodical generation of master key on the base station and periodical generation of round key on the sensors to prevent the brute-force attacks. An overall comparison of cipher techniques with respect to energy, time, memory and security concludes the suitability of MBCC algorithm for resource constraint wireless sensors with security requirements.

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