Smart Home Security System Based on Zigbee

Liang, Chua Boon; Tabassum, Mujahid; Kashem, Saad Bin Abul; Zama, Zulfiqar; Suresh, P.; Saravanakumar, U.

doi:10.1007/978-981-15-5029-4_71

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…In contrast to electoral monitoring by observers, CCTV-based monitoring, given its inherent data-oriented nature, enhances the role that AI can play in election monitoring, which is what makes this a topic of interest for this paper. The emergence of scalable AI that can identify events in real-time has been increasingly used in other contexts such as institutional security (Kakadiya et al 2019), home alert systems (Liang et al 2021), and health-motivated monitoring of elder cit-izens (Huang et al 2018); this makes the video-based election monitoring context a fertile avenue for AI usage.…”

Section: The Contextmentioning

confidence: 99%

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AI and core electoral processes: Mapping the horizons

Simoes,

MacCarthaigh

2023

AI Magazine

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It is well documented that there has been significant enthusiasm across the globe in respect of using AI for all forms of social activity. However, the electoral process – the time, place, and manner of elections within democratic nations – is one of few sectors in which there has been limited penetration of AI. Electoral management bodies in many countries have recently started exploring and deliberating over the use of AI in the electoral process. In this paper, we consider five avenues within the core electoral process which have potential for AI usage, and map the challenges involved in using AI within them. These five avenues are: voter list maintenance, determining polling booth locations, polling booth protection processes, voter authentication, and video monitoring of elections. Within each avenue, we lay down the context, illustrate current or potential usage of AI, and discuss extant or potential ramifications of AI usage, as well as potential directions for mitigating risks when considering AI usage. We believe that the scant current usage of AI within electoral processes provides a very rare opportunity to deliberate on the risks and mitigation possibilities prior to actual and widespread AI deployment. This paper is an attempt to map the horizons of risks and opportunities in using AI within electoral processes and to help shape the debate around the topic.

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Section: The Contextmentioning

confidence: 99%

“…2019), home alert systems (Liang et al. 2021), and health‐motivated monitoring of elder citizens (Huang et al. 2018); this makes the video‐based election monitoring context a fertile avenue for AI usage.…”

Section: Video Monitoring Of Electoral Fraudmentioning

confidence: 99%

AI and core electoral processes: Mapping the horizons

Simoes,

MacCarthaigh

2023

AI Magazine

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“…The sensor network consists of multiple sensor nodes deployed in the smart home, forming a multi-hop self-organizing network using wireless communication. Liang et al [29] built a wireless sensor network for the smart home security system based on ZigBee protocol and other IoT technologies. Their proposed sensor network mainly consists of a digital access control system(hub) and sensor nodes.…”

Section: Related Work 21 the Construction Of Multi-channel Sensor Net...mentioning

confidence: 99%

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Zhang,

Ananda,

et al. 2023

Proceedings of the Eleventh International Symposium of Chinese CHI

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Both sensor networks and data fusion are essential foundations for developing the smart home Internet of Things (IoT) and related fields. We proposed a multi-channel sensor network construction method involving hardware, acquisition, and synchronization in the smart home environment and a smart home data fusion method (SHDFM) for multi-modal data (position, gait, voice, pose, facial expression, temperature, and humidity) generated in the smart home environment to address the configuration of a multi-channel sensor network, improve the quality and efficiency of various human activities and environmental data collection, and reduce the difficulty of multi-modal data fusion in the smart home. SHDFM contains 5 levels, with inputs and outputs as criteria to provide recommendations for multi-modal data fusion strategies in the smart home. We built a real experimental environment using the proposed method in this paper. To validate our method, we created a real experimental environment -a physical setup in a home-like scenario where the multi-channel sensor network and data fusion techniques were deployed and evaluated. The acceptance and testing results show that the proposed construction and data fusion methods can be applied to the examples with high robustness, replicability, and scalability. Besides, we discuss how smart homes with multi-channel sensor networks can support digital twins.

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“…Faisal et al purposed his research while keeping the focus on the security of homes against fire hazard and used multiple wireless sensors in order to detect fire and integrated the GSM technology to send a notification if such an event occurs [22,23]. Our system takes the data from the sensor, but for accuracy we use data for analysis in the AI fuzzy logic algorithm to provide efficient results.…”

Section: Related Workmentioning

confidence: 99%

Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)

et al. 2021

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Fire monitoring systems have usually been based on a single sensor such as smoke or flame. These single sensor systems have been unable to distinguish between true and false presence of fire, such as a smoke from a cigarette which might cause the fire alarm to go off. Consuming energy all day long and being dependent on one sensor that might end with false alert is not efficient and environmentally friendly. We need a system that is efficient not only in sensing fire accurately, but we also need a solution which is smart. In order to improve upon the results of existing single sensor systems, our system uses a combination of three sensors to increase the efficiency. The result from the sensor is then analyzed by a specified rule-set using an AI-based fuzzy logic algorithm; defined in the purposed research, our system detects the presence of fire. Our system is designed to make smart decisions based on the situation; it provides feature updated alerts and hardware controls such as enabling a mechanism to start ventilation if the fire is causing suffocation, and also providing water support to minimize the damage. The purposed system keeps updating the management about the current severity of the environment by continually sensing any change in the environment during fire. The purposed system proved to provide accurate results in the entire 15 test performed around different intensities of a fire situation. The simulation work for the SMDD is done using MATLAB and the result of the experiments is satisfactory.

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Smart Home Security System Based on Zigbee

Cited by 12 publications

References 5 publications

AI and core electoral processes: Mapping the horizons

AI and core electoral processes: Mapping the horizons

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)

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