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Blockchain 3.0 technology supports the evolution for E-Government (EG) to become Web 3.0 oriented by providing the infrastructure, services, and processes needed alongside Information and Communication Technologies [21] such as Artificial Intelligence (AI) agents to secure and enhance communication between governments, businesses, and citizens [22]. EG 3.0 is totally dependent on Information Communication Technologies (ICT) to evolve along with Web 3.0 technologies, such as blockchain, artificial intelligence, semantic web and text analytics, machine learning, internet of things, and big data analytics [23].
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Dynamic SCs embed various rules that allow them to perform different actions. Examples of dynamic SCs include functions that monitor certain conditions and trigger intended actions. For example, when a dynamic SC monitors electricity consumption and temperatures logged on the BC of an energy-smart building. The dynamic SC includes thresholds for heating and consumption measurements to adjust temperatures in an eco-friendly way designed to avoid excessive electricity consumption and cost. The following pseudocode offers the logic behind monitoring and execution:
if room_temperature < 18 Celcius {
if electricity_consumption < 25 Watt
then turn_on air_conditioner
else send_message: "The daily
electricity consumption threshold has been reached. Would you like to turn on the A/C?"
if user_answer == ‘YES’ then
turn_on air_conditioner
else do_nothing }
if room_temperature > 25 Celcius {
if electricity_consumption < 25 Watt
then turn_on heating_unit
else send_message: "The daily electricity
consumption threshold has been reached. Would you like to turn on the heating_unit?"
if user_answer == ‘YES’ then
turn_on heating_unit
else do_nothing }
This dynamic SC, although deterministic, follows a non-static conditioned flow , which that shows how a dynamic SC might be formed and how it can act. This The code is simplistic and computer functions can be long and complex. Additionally, the example involves human interaction which, on occasion, may hinder or cancel the dynamic action feature of SCs. In our perspective, human the SC. Human input is considered dynamic in terms of a non-standard, condition-driven final action. The dynamic nature of SCs may also be controlled with machine-to-machine (M2M) actions. Unpredictable outcomes may occur if a developer’s design and implementation of the SC are erroneous, incomplete, or non-deterministic.
Another approach to dynamic SC EG 3.0 applications is to interconnect public administrations that request to exchange citizen data. For example, if a tax service requests access to citizen land titles held by a land registry service. A dynamic SC supplied with a tax service VAT number may access land titles tied to that VAT number, if if appropriate citizen permissions are in place. If a universal BC ledger contains land titles for all citizens, a dynamic SC may help to confront fraud and tax evasion and mediate the secure exchange of data between nations.
Oracle driven
Both static and dynamic SCs handle data that reside resides on the BC. The Oracle, the third major category of SC, is oracle designed to work with data from sources external to the BC. Oracle SCs are dynamic and include information brought in by the so-called AI oracles, which are also smart contracts. Oracle SCs OracleSCs act as AI agents with the ability to request information from the real world and write it on the blockchain for other smart contracts to consume [24]. What is special about the oracle SC category is that SCs are generally not allowed to incorporate data external to the BC due to the determinism of BC functions. Determinism states the same result must be returned each time an SC function is called and external resources are often subject to change. Determinism is typically enforced by only utilizing data that exists as the ledger’s state. An exception is made through oracles that to write data on the BC to represent that represents the ledger state at the exact time the data was is written on the ledger.
AI oracle SCs apply EG 3.0 to law applications. For example, laws for inheritance can change and notaries or other public servants in an oversight role must be formally informed regarding issues such as legacy transfer. An AI oracle accesses information from a government repository and writes to the BC when a specific law changes. After which After this, a notification is sent through a BC 3.0 application to prove date and time sent, to inform interested parties, and to request and record confirmation of receipt on the BC.
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Government 3.0
EG, by [25] definition, is the use of ICT to provide a means for governments, citizens, and businesses to interact, communicate, share information, and deliver services to various stakeholders. EG 1.0 utilized the World Wide Web and available ICTs to strive toward efficiency [26]. EG 2.0, through portal services supported by Web 2.0 technologies, became more citizen-centric, promoting citizen participation and enhancing e-democracy [27]. The technological evolution shaping EG , infers EG 3.0 will use Web 3.0 ICTs such as distributed ledger technology (DLT), AI, Semantic Web, and the World Wide Virtual Web [20][28].
Artificial Intelligence is a promising and disruptive technology. AI’s technological ability to equip machines with cognitive capabilities to that learn, infer, and adapt per consumed data is reinforced by the amount of information produced by smart devices, social media, and web applications [29]. One problem governments, organizations, and companies face in leveraging this amount of information is centralization and provenance, the latter related to information source legitimacy and authenticity. Data in AI projects is are centrally controlled , and can be tampered with. For example, Microsoft’s AI Twitter-based bot project was overwhelmed with racist remarks which, unfortunately, bots repeated to users [30].
One argument under consideration [22] offers AI as the solution to major governmental obstacles, particularly related to issues such as resource allocation, large datasets, experts shortage, predictable scenarios, procedural and repetitive tasks, and diverse data aggregation and summarization. Crucial to research is an analysis of how to overcome centralization, provenance, and authenticity problems. The combination of BC and AI technologies can address current centralization problems and, in parallel, provide solutions for resource optimization and and return private, personal data control back to their respective owners in a distributed, decentralized, and democratized manner [31].
The remainder of this paper examines two EG 3.0 scenarios supported by BC 3.0 and AI technology, the purpose of which is to provide EG stakeholders and policy makers policymakers avenues to exploit current industry BC and AI applications for governmental, public, and social good.
Energy data – Scenario1
In recent years, digital smart city governance with ICT expanded and regional research addressed the increased energy demand that emanated from the multiplication and complexity of Internet of Things (IoT) devices. It became crucial for local governments to practice energy management strategies and use available energy efficiently [32]. A modern smart city applies smart technologies to its infrastructures and to citizen residences. The EG 3.0 scenario includes IoT devices, installed at citizen residences, that produce energy; these citizens are referred to as prosumers. This ability of energy consumers to produce energy from renewable sources and distribute that energy, through smart grids as prosumers, increases the difficulty of national energy management. However, prosumers also create the opportunity for smart city energy sustainability and efficiency when citizen produced energy is successfully modelled modeled and incorporated into city energy systems along with energy related to transportation and facilities [33]. Energy management is critical; the European Commission, in the last two years, published two directives for energy efficiency goals with a 20% energy savings target by 2020 and a 30% energy efficiency target for 2030. Additional, specific national targets include lowering energy bills, reducing nation’ reliance on external suppliers, and eco-friendly protecting the environment [42][43]. EG 3.0 supports citizen-sourcing, increases efficiency in all phases of the energy supply, and leads energy sector management. BC 3.0 technology, in conjunction with AI, provides authentication, decentralized intelligence, security, and collective decision making.
In EG 3.0, IoT devices produce energy data that is stored on a private permissioned blockchain. Data stored on a BC is tamper-proof; it is cryptographically immutable and authenticated because each transaction is digitally signed. Energy data is considered confidential, security concerns must be mitigated by using with a private permissioned BC. Know Your Customer (KYC) compliance is enforced through permission policies on the BC network; each citizen determines what personal information or energy production data are is shared. Additional security is realized when prosumer registration applicants follow a strict protocol and participate in local energy networks logged on the BC. This prosumer energy approach is automated with Dynamic SCs controlling the processes of IoT data logging, registration and approval logging, and available energy dispatching and monitoring.
A SC collects energy consumption and production measurements from prosumer IoT devices and logs them on the blockchain. The prosumer provides the BC login identity issued to her in. This self-sovereign identity (SSI) ensures secure entry and prosumer user control [44]. A SC dispatches surplus energy from a prosumer residence to the main energy system or to a citizen-sourced smart grid. If, for example, daily consumption need is 14kWh and the SC detects the power produced from renewable sources exceeds 15kWh, an action automatically triggers and dispatches this available surplus power to a pre-identified local energy system. A dynamic smart contract deposits the required, predefined payment for the energy dispatched to the prosumer’s account. Oracle-based SCs inform citizens how their energy dispatching can be more profitable and provide incentives for participants on the energy network. Smart grids, informed by local policies, consider geographic factors, energy needs, and building production capabilities. AI agents operate at citizen residences as collective decision-making mechanisms that apply Swarm Intelligence (SI) and achieve swarm goals [34]. SI calculates how much energy can be dispatched to a city’s central energy system and how much energy is available to be traded among smart grid participants. AI EG applications read data written on the BC and forecast city energy needs for hours, days, or months. AI analyzes data for trends or peak hours. The results and metadata from AI analyses are grouped per district to help governments and policy makers policymakers create more efficient energy management strategies as they achieve local and national goals.
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