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The number of the applications that analyze and evaluate human activities of daily living such as transport mode detection and activity recognition is increasing rapidly due to the requirements in several fields such as transportation planning, elderly care and ambient assisted living. One of the drawbacks of these systems is their high battery consumption characteristics. In this study, we introduce a novel instance selection methodology that provides energy saving in testing process by reducing the amount of the training data while preserving the accuracy of the system. By their nature, daily living activities separate to several sub-classes within each class. The proposed method selects instances in an iterative cluster-based manner assorting with the characteristic structure of the daily activities. The success of the system is evaluated by applying Decision Tree (J48) and k-Nearest Neighborhood (k-NN) algorithms to two different publicly available daily activity datasets. Obtained results show that the proposed instance selection algorithm based on sub-activity characteristics could achieve up to 11% improvement of the classification results when 50% of the training instances are eliminated. With the help of this selection process, we built 4% to 57% smaller and 4% to 62% faster models for activity recognition on energy-constrained devices. [ABSTRACT FROM AUTHOR]

Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM) mechanism for resource constrained IoT devices using IEEE 802.11ah has not been proposed as yet. We therefore propose a new AKM mechanism for an IoT access network, which is based on IEEE 802.11 key management with the IEEE 802.1X authentication mechanism. The proposed AKM mechanism does not require any pre-configured security information between the access network domain and the IoT service domain. It considers the resource constraints of IoT devices, allowing IoT devices to delegate the burden of AKM processes to a powerful agent. The agent has sufficient power to support various authentication methods for the access point, and it performs cryptographic functions for the IoT devices. Performance analysis shows that the proposed mechanism greatly reduces computation costs, network costs, and memory usage of the resource-constrained IoT device as compared to the existing IEEE 802.11 Key Management with the IEEE 802.1X authentication mechanism. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensor networks have become an emerging technology in industries, environment monitoring and health care monitoring systems and so on. However, sensor node is a resource‐constrained device in terms of memory, bandwidth and energy. These constraints impose congestion in the network, leading to large number of packet drops, low throughput and significant wastage of energy because of retransmission. This study presents a new approach for predicting congestion using probabilistic method, and controlling congestion using new rate control methods. The probabilistic approach used for the prediction of occurrence of congestion in a node is developed using data traffic and buffer occupancy. The rate control method uses rate allocation schemes, namely, rate reduction (RR), rate regulation (RRG) and split protocol (SP) to improve throughput and to reduce packet drops. In addition, an energy‐efficient routing which finds the best forwarding node for data transmission is also proposed. Simulation results are compared with decentralised predictive congestion control (DPCC). The results show that the proposed method indeed reduces congestion and energy consumption, and improves the performance. [ABSTRACT FROM AUTHOR]