“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

LR 3: Link Reliable Reactive Routing Protocol for Wireless Sensor Networks

Existing reliable-oriented routing protocols computes link reliability based on the packet reception ratio and neglects the impact of various parameters such as noise, shadowing, battery-lifespan, uncertainty and geographic locations. In this paper, we propose a Link Reliable Reactive Routing(LR3) protocol for WSNs to accomplish reliability and resilience to out-of-order transmission and path diversity at each hop. The log-normal shadowing model is used to estimate link reliability and a back-off scheme is used to determine delay. A new cost function is estimated to find forwarding nodes on the mentor path that includes link reliability, delay, status of queue, and packet advancement at the forwarding node. LR3 is simulated using NS-2 and the results show that it outperforms other reactive routing protocols in terms of packet delivery ratio, latency, link reliability and data transmission cost[1][2]

Data Aggregation over Geographical Area Coverage in Wireless Adhoc and Sensor Networks

Wireless sensor networks has become envisage for various purpose in all the fields of technology. Random deployments are a fundamental crisis to realize coverage and/or connectivity in sensor networks. Path-Trace-Back protocol is proposed proficiently to preserve the data at specific geographic area coverage in a network with node mobility in nature. This scheme can turn out to be more difficult for isolated places in the area where only low density of sensor nodes exist. To tackle this problem, the path-trace-back scheme, initially permits a mobile node to periodically return the data to fixed location about the trail area coverage whose packets travel from the region of source and utilizes the reported trail to enhance the path revisited. Based on the optimality conditions, we devise the distance based and trail based approach for an explicit area using Path-trace -- back protocol that shows an enhancement of 80 percent against the distance based and Max Propagation protocol. The result obtained achieves higher performance when the region of coverage area is less connected

Geographic opportunistic routing protocol based on two-hop information for wireless sensor networks

To improve energy efficiency and packet delivery ratio in WSNs. We propose a two-hop geographic opportunistic routing (THGOR) protocol that selects a subset of 2-hop neighbours of node which have higher packet reception ratio and residual energy as the next forwarder node, it also selects 1-hop neighbours that have excellent coverage of 2-hop neighbours as relay nodes. The proposed protocol is compared with geographic opportunistic routing (GOR) and efficient QoS-aware geographic opportunistic (EQGOR). GOR and EQGOR select a forwarding sensor node on the basis of distance. However, GOR and EQGOR results in lower packet delivery rate, and quick depletion of energy. The THGOR is comprehensively evaluated through NS-2 and compared results with EQGOR and GOR. Simulation results show that THGOR significant improvement in packet advancement, reliable transmission, energy efficiency and optimises packet transmission delay

RRDVCR: Real-time reliable data delivery based on virtual coordinating routing for Wireless Sensor Networks

Real-time industrial application requires routing protocol that guarantees data delivery with reliable, efficient and low end-to-end delay. Existing Routing(THVR) 13 is based velocity of Two-Hop Velocity and protocol relates two-hop velocity to delay to select the next forwarding node, that has overhead of exchanging control packets, and depleting the available energy in nodes. We propose a Real-Time Reliable Data delivery based on Virtual Coordinates Routing (RRDVCR) algorithm, based on the number of hops to the destination rather than geographic distance. Selection of forwarding node is based on packet progress offered by two-hops, link quality and available energy at the forwarding nodes. All these metric are co-related by dynamic co-relation factor. The proposed protocol uses selective acknowledgment scheme that results in lower overhead and energy consumption. Simulation results shows that there is about 22% and 9.5% decrease in energy consumption compared to SPEED 8 and THVR 13 respectively, 16% and 38% increase in packet delivery compared to THVR 13 and SPEED 8 respectively, and overhead is reduced by 50%. © 2016 IEEE

Location-aware IoT Search Framework based on Data Messaging and Aggregation Techniques

With the deeper penetration of the Internet of Things (IoT) devices into the physical infrastructure and wider acceptance of IoT technologies by the community has created a tremendous opportunity for designers and developers to put forth applications that aim to improve the present state-of-the-art solutions. Location-based Service (LBS) provisioning is one such area where location data is utilized to offer user-centric services and thus improve personalized user experience. In this paper, we propose a search framework for the IoT ecosystem that offers location-aware services based on data messaging and aggregation techniques. We design a taxonomy for the classification of the IoT devices based on their mobility frequency and leverage it to design a priority scheme to address the co-located devices that offer similar services. Experimental results show that our proposed LBS Provisioning System is more effective

THGOR: Two-Hop Geographic Opportunistic Routing in WSNs

Geographic Opportunistic Routing selects a forwarding sensor node to progress data packets on the basis of geographic distance. The multipath routing uses multiple paths to achieve both reliability and delay. However, geographic opportunistic routing results in lower packet delivery rate and high latency. The multipath routing introduces channel contention, interference and quick depletion of energy of the sensor node in a asymmetric link wireless environment. The existing work Efficient QoS aware Geographic Opportunistic (EQGOR) elects and prioritize the forwarding nodes to achieve different QoS parameters. However, in EQGOR, the count of forwarding nodes increases with the increase in the required reliability. To improve energy efficiency, delay, and successful ratio of packet delivery in WSNs, we propose a Two-Hop Geographic Opportunistic Routing (THGOR) protocol that selects a subset of 2-hop neighbors of node which has high packet reception ratio and residual energy at the next forwarder node, and the selected 1-hop neighbors of node has supreme coverage of 2-hop neighbors as relay nodes. THGOR is comprehensively evaluated through ns-2 simulator and compared with existing protocols EQGOR and GOR. Simulation results show that THGOR significant improvement in packet advancement, delay, reliable transmission and energy efficient