Robots can be equipped with a range of senses to allow them to perceive and interact with the world in a more natural and intuitive way. These senses can include vision, hearing, touch, smell, and taste. Vision allows the robot to see and recognize objects and navigate its environment. Hearing enables the robot to recognize sounds and respond to vocal commands. Touch allows the robot to perceive information about the texture, shape, and temperature of objects through the sense of touch. Smell enables the robot to recognize and classify different odors. Taste enables the robot to identify the chemical composition of materials. The specific senses used in a robot will depend on the needs of the application, and many robots use a combination of different senses to perceive and interact with the environment. This paper reviews the five senses used in robots, their types, how they work, and other related information, while also discussing the possibility of a Sixth Sense.

The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Localisation is one of the most important services in sensor networks. The impact of the used localisation technique used reflects on wireless sensor network services such as coverage or event detection. Accuracy is considered to be one of the factors that is related to successful network performance in most sensor network applications. Communication ranging is one of the practical applicable technique for localisation on short‐range communication nodes with no additional components. This study proposes an enhanced communication ranging localisation technique with high accuracy. The authors evaluated and validated the proposed technique in a forest environment using 20 Waspmote Nodes and Meshlium router (Spanish product). The novelty of the proposed localisation technique is based on controlling communication range by manipulating the XBee transmitter power changing ability on four rounds to provide the best‐predicted location. The results demonstrated the locations of nodes were found with error range between 0 and 22 m. The network location error decreased starting with 30 m in round 1 down to 8 m in round 4. The technique can be applied to different types of networks and technologies as long as the transmission power can be set to different values and the communication range is known or can be measured manually.

The rapid expansion of Internet of Things (IoT) devices underscores the critical importance of robust security protocols, particularly in the realm of children's toys. This study introduces an innovative multi-factor authentication strategy integrating Quick Response (QR) codes with Blockchain technology to fortify the security of IoT toys designed for children. The primary objective is to safeguard young users against potential threats stemming from unauthorized access, thereby ensuring a secure interaction with IoT-enabled toys. By amalgamating authentication factors, including QR codes, the proposed approach establishes a multilayered security framework. Leveraging the inherent immutability and transparency of Blockchain, the system verifies the authenticity of IoT toys by scanning a unique QR code, thus mitigating risks associated with malwares and unauthorized access. The decentralization of Blockchain ensures no single point of failure, enhancing resilience against cyber threats. Extensive usability studies underscore the efficacy and practicality of the advanced multi-factor authentication solution, poised to elevate the safety standards of IoT toys in the digital age. This innovative approach not only bolsters security but also fosters trust among users, enabling seamless and worry-free interaction with IoT-enabled toys for children worldwide.