Data gathering is among the issues constantly acquiring attention in the area of wireless sensor networks (WSNs). There is a consistent increase in the research directed on the gains of applying mobile elements (MEs) to collect data from sensors, especially those oriented to power issues. There are two prevailing strategies used to collect data in sensor networks. The first approach requires data packets to be serviced via multi-hop relay to reach the respective base station (BS). Thus, sensors will send their packets through other intermediate sensors. However, this strategy has proven to consume high and a substantial amount of energy due to the dependency on other nodes for transmission. The second approach encompasses a ME which serves as the core element for the searching of data. This ME will visit the transmission range of each sensor to upload its data before eventually returning to the BS to complete the data transmission. This approach has proven to reduce the energy consumption substantially as compared to the multi-hop strategy. However, it has a trade-off which is the increase of delay incurred and is constrained by the speed of ME. Furthermore, some sensors may lose their data due to overflow while waiting for the ME. In this paper, it is proposed that by strategically divisioning the area of data collection, the optimization of the ME can be elevated. These derived area divisions are focused on the determination of a common configuration range and the correlation with a redundant area within an identified area. Thus, within each of these divided areas, the multi-hop collection is deployed as a subset to the main collection. The ME will select a centroid point between two sub-polling points, subsequently selecting common turning points as the core of the basis of the tour path. Extensive discrete-event simulations have been developed to assess the performance of the proposed algorithm. The acquired results depicted through the performance metrics of tour length and latency have determined the superior performance of the proposed algorithm in comparison to the existing strategy. In addition, the proposed algorithm maintains the energy consumption within an acceptable level.

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Providing a seamless handover in the Internet of Thing (IoT) applications with minimal efforts is a big challenge in mobility management protocols. Several research efforts have been attempted to maintain the connectivity of nodes while performing mobility-related signalling, in order to enhance the system performance. However, these studies still fall short at the presence of short-term continuous movements of mobile nodes within the same network, which is a requirement in several applications. In this paper, we propose an efficient group-based handoff scheme for the Mobile Nodes (MNs) in order to reduce the nodes handover during their roaming. This scheme is named Enhanced Cluster Sensor Proxy Mobile IPv6 (E-CSPMIPv6). E-CSPMIPv6 introduces a fast handover scheme by implementing two mechanisms. In the first mechanism, we cluster mobile nodes that are moving as a group in order to register them at a prior time of their actual handoff. In the second mechanism, we manipulate the mobility-related signalling of the MNs triggering their handover signalling simultaneously. The efficiency of the proposed scheme is validated through extensive simulation experiments and numerical analyses in comparison to the state-of-the-art mobility management protocols under different scenarios and operation conditions. The results demonstrate that the E-CSPMIPv6 scheme significantly improves the overall system performance, by reducing handover delay, signalling cost and end-to-end delay.

Underwater Acoustic Sensor Network (UASN) is playing a vital role in exploration and monitoring operations in underwater environments that are difficult to reach. The communication medium relies on acoustic waves, which limits the performance of UASN in comparison to terrestrial sensor network. This is due to its limited bandwidth, high latency and high path loss. In this article, a comprehensive study is presented to show the characteristics of UASN. This study is designed to investigate the performance of UASN in different scenarios. The investigation encompasses End- To-End delay and energy consumption with respect to different parameters. In addition, we analyze the performance of underwater acoustic ad-hoc networks in the presence of node distribution and network dimensions.

Data gathering is a focal task in wireless sensor networks (WSNs) that expends most of the sensor nodes' energy. Two factors that are considered essential in data gathering are latency and power consumption. The multihop data gathering approach proves that latency is minimized due to the speed of forwarding data to the base station.However, this may lead to increased power consumption and increased possibility of an emerging hotspot area. In contrast, data gathering based on a mobile element (ME) proves that power consumption is minimized due to avoiding relay data in extreme schemes. However, this may increase the latency of data gathering due to the low velocity of the ME. In this article, an efficient hybrid data gathering algorithm called zonal data gathering based on multihop and ME in WSNs (ZDG-MME) is proposed. In ZDG-MME, intelligent data gathering is proposed, capturing the unique nature of nodes along with the node's position. In addition, it is able to forward the tailored data to the base station by segmenting the deployment field into two divisions. First, the inner division, which is the closest to the base station, reports the sensed data through multihop communications. Second, the outer division reports the data to certain nodes that locally buffer the data from affiliated sensors and await the ME for uploading. Furthermore, ZDG-MME analyzes the sensing area in a way to ensure balancing between latency and power consumption based on application requirements while avoiding the hotspot area. An extensive simulation clarifies the validity and effectiveness of the proposed approach in terms of ME tour length, data gathering latency, and total energy consumption.

People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses. However, chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope. Chatbots employ Natural Language Understanding (NLU) to infer their responses. There is a need for a chatbot that can learn from inquiries and expand its area of experience with time. This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval. This study proposes a methodology to enhance a chatbot's brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles. We used a COVID-19 information dataset to evaluate the proposed methodology. The pandemic has been accompanied by an "infodemic" of fake news. The chatbot was evaluated by a medical doctor and a public trial of 308 real users. Evaluations were obtained and statistically analyzed to measure effectiveness, efficiency, and satisfaction as described by the ISO9214 standard. The proposed COVID-19 chatbot system relieves doctors from answering questions. Chatbots provide an example of the use of technology to handle an infodemic.

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