This page has only limited features, please log in for full access.
A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.
Muhammad Diyan; Murad Khan; Bhagya Nathali Silva; Kijun Han. Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning. Sensors 2020, 20, 5498 .
AMA StyleMuhammad Diyan, Murad Khan, Bhagya Nathali Silva, Kijun Han. Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning. Sensors. 2020; 20 (19):5498.
Chicago/Turabian StyleMuhammad Diyan; Murad Khan; Bhagya Nathali Silva; Kijun Han. 2020. "Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning." Sensors 20, no. 19: 5498.
Vehicular ad hoc network (VANET) is a special form of mobile ad hoc network (MANET), which plays a key role in the intelligent transportation system (ITS). Though many outstanding geographic routing protocols are designed for VANETs, a majority of them use parameters that only affect routing performance. In this article, we propose an intersection routing based on fuzzy multi-factor decision (IRFMFD), which utilizes several features. The scheme is divided into two parts, namely vehicular decision management and intersection decision management. In the vehicular component, candidate vehicles between two static nodes (SNs) located at two intersections derive potential routing paths considering distance, neighbor quantity, and relative velocity. In the intersection component, the candidate SN was chosen from the current intersection’s 2-hop neighbors which were connected with the current intersection by a route that was decided on in part one. To get the best scheme, we also introduced other factors to estimate the number of hops in each link and link lifetime. The simulation shows that the IRFMFD outperforms on delivery ratio and end-to-end delay compared with AODV (Ad hoc on-demand distance vector), GPSR (Greedy perimeter stateless routing) and GeOpps (Geographical opportunistic routing).
Zhenbo Cao; Bhagya Nathali Silva; Muhammad Diyan; Jilong Li; Kijun Han. Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs. Applied Sciences 2020, 10, 6613 .
AMA StyleZhenbo Cao, Bhagya Nathali Silva, Muhammad Diyan, Jilong Li, Kijun Han. Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs. Applied Sciences. 2020; 10 (18):6613.
Chicago/Turabian StyleZhenbo Cao; Bhagya Nathali Silva; Muhammad Diyan; Jilong Li; Kijun Han. 2020. "Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs." Applied Sciences 10, no. 18: 6613.
Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.
Muhammad Diyan; Bhagya Nathali Silva; Kijun Han. A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning. Sensors 2020, 20, 3450 .
AMA StyleMuhammad Diyan, Bhagya Nathali Silva, Kijun Han. A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning. Sensors. 2020; 20 (12):3450.
Chicago/Turabian StyleMuhammad Diyan; Bhagya Nathali Silva; Kijun Han. 2020. "A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning." Sensors 20, no. 12: 3450.
The requisition for electrical energy, smart grid, and renewable energy paradigm extend a new space for Electrical Energy Data Management and Processing Systems (EEDMS), in such a way that can mitigate the consumption of electrical energy. Similarly, the implementation and maintenance of the EEDMS is a challenging task. Moreover, the heterogeneous energy data generated from residential and commercial sector are the leading challenges for standard Internet of Things (IoT) architecture. This contributes enormous energy data preprocessing and analyzing solutions to IoT landscape. To overcome these challenges, we present a scalable multitasking Internet of Things Gateway (IoTGW) for the modern era of IoT by placing reliance on a new entity called Data Loading and Storing Module (DLSM). The provided DLSM module combine with the Gateway module services like orchestrator, flexibility of bridging front end grid, back end grid and fast formatted data trade between sensing domain and application domain enables a high dynamic distributed framework. Specifically, we add Adaboost‐Multilayer Perceptron hybrid data classifier module to the proposed work to enhance service provision of IoT gateway toward various IoT application services and protocols to facilitate IoT demands such as multitasking, interoperability, classification, and fast data delivery between different modules. IoTGW is implemented and tested using a real‐time IoT data streaming network. The experimental results confirms the superiority of proposed work in terms of scalability to serve novel applications and facilitate broad scope of IoT.
Muhammad Diyan; Bhagya Nathali Silva; Jihun Han; Zhenbo Cao; Kijun Han. Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing. Transactions on Emerging Telecommunications Technologies 2020, 1 .
AMA StyleMuhammad Diyan, Bhagya Nathali Silva, Jihun Han, Zhenbo Cao, Kijun Han. Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing. Transactions on Emerging Telecommunications Technologies. 2020; ():1.
Chicago/Turabian StyleMuhammad Diyan; Bhagya Nathali Silva; Jihun Han; Zhenbo Cao; Kijun Han. 2020. "Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing." Transactions on Emerging Telecommunications Technologies , no. : 1.