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This paper analyzes the trade-off issue between energy efficiency and packet delivery latency among existing duty-cycling MAC protocols in wireless sensor networks for low data-rate periodic-reporting applications. We then propose a novel and practical wake-up time self-Learning MAC (L-MAC) protocol in which the key idea is to reuse beacon messages of receiver-initiated MAC protocols to enable nodes to coordinate their wakeup time with their parent nodes without incurring extra communication overhead. Based on the self-learning mechanism we propose, L-MAC builds an on-demand staggered scheduler to allow a node to forward packets continuously to the sink node. We present an analytical model, and conduct extensive simulations and testbed experiments to show that L-MAC achieves significant higher energy efficiency compared to the state-of-the-art asynchronous MAC protocols and a similar result of latency compared to synchronous MAC protocols. In particular, under QoS requirements with an upper bound value for one-hop packet delivery latency within 1 s and a lower bound value for packet delivery ratio within 95%, results show that the duty cycle of L-MAC is improved by more than 3.8 times and the end-to-end packet delivery latency of L-MAC is reduced by more than 7 times compared to those of AS-MAC and other state-of-the-art MAC protocols, respectively, in case of the packet generation interval of 1 minute. L-MAC hence achieves high performance in both energy efficiency and packet delivery latency.

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model.

The IEEE 802.15.4e standard is an amendment of the IEEE 802.15.4-2011 protocol by introducing time-slotted channel hopping access behavior mode. However, the IEEE 802.15.4e only defines time-slotted channel hopping link-layer mechanisms without an investigation of network formation and communication scheduling which are still open issues to the research community. This article investigates the network formation issue of the IEEE 802.15.4e time-slotted channel hopping networks. In time-slotted channel hopping networks, a joining node normally takes a long time period to join the network because the node has to wait until there is at least one enhanced beacon message advertised by synchronized nodes (synchronizers) in the network on its own synchronization channel. This leads to a long joining delay and high energy consumption during the network formation phase, especially so in highly dynamic networks in which nodes join or rejoin frequently. To enable a rapid time-slotted channel hopping network formation, this article proposes a new design for slotframe structure and a novel adaptive joining scheme based on fuzzy logic. Our proposed scheme enables a synchronizer to be able to adaptively determine an appropriate number of enhanced beacons it should advertise, based on the number of available synchronizers in the network, so that joining nodes can achieve a short joining time while energy consumption of enhanced beacon advertisement at the synchronizers is optimized. Through extensive mathematical analysis and experimental results, we show that the proposed scheme achieves a significant improvement in terms of joining delay compared to state-of-the-art studies.

This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud.

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