# Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
## 3 Emerging Technologies Enabling Positive Energy Districts
### Technological Challenges in Positive Energy Districts
However, interdependencies between energy GM, CM, and EMS topologies lack a distinct developed path and established target parameters to predict future directions for EMS research and to determine robust evaluation parameters for EMS proposals. [PERSON] et al. [79] proposed blockchain-based architectures as a potential solution, which are well aligned with incoming decentralized GM and the limitations of CM requirements. Blockchain-based architectures offer adaptability, resilience, scalability, and security and can be considered an effective and efficient choice for a next-generation EMS. (iii) _Communication, data, and physical interoperability:_ In smart PEDs, digital systems are deployed that contain diverse service providers who collaborate to provide digital services to citizens. Due to lack of standardization of interfaces in isolated systems (vertical silos), they are seen as barriers regarding interoperability and the seamless transfer of data between systems. [PERSON] [80] proposed the use of an Application Programming Interface (API) to enable seamless communication interoperability (interface, technology), data interoperability (syntactic, semantic), and physical interoperability (technical, network, device, platform). Another challenge is the interoperability of multiple neighborhood systems, which mainly stem from the incompatibility of the different systems used in the creation of a PED. (iv) _Wireless sensor network (WSN) challenges_[78]:
* WSN security issues lead to a lack of communication between sensors and waste more energy. WSNs are disposed to failure due to their huge number of nodes and unique restrictions in both hardware and software [81]. The need for efficient solutions has increased, particularly with the rise of the IoT, which relies on the effectiveness of WSNs. Exploration of ML algorithms together with fuzzy logic has been proposed to increase reliability, particularly in mitigating node and link failures. * Privacy issues may make WSNs risky [82]. Challenges such as privacy threats posed by smart objects, restriction regarding collecting and processing confidential data, and the regulation of using and distributing confidential data collected need to be considered. When creating new systems and services, users' requirements should be carefully examined and sensitive data should be responsibly handled. * Sustainable operations in energy-constrained environments. To improve WSN performance, energy harvesting (EH) from environmental forces (Radio Frequency (RF) signal, sunlight, vibrations, wind, etc.) has been identified as a successful method for obtaining unlimited WSN energy [83]. EH-WSNs maxi
mize the utilization of harvested energy by fine-tuning the operations of sensor nodes according to obtainable power. EH-WSNs enable wireless sensor nodes to prolong their lifetime and to minimize their dependence on constrained energy resources. Also, cognitive radio (CR) technologies are considered to improve spectrum efficiency and to enable dynamic spectrum access by identifying underutilized frequency bands. Security is a challenge in EH technologies. Physical layer security (PLS) has been proposed as a solution because it embeds security in the process of energy harvesting, and as a result, replaces classical encryption techniques [83]. * Time synchronization ensures that all nodes in a WSN share a common time reference, which is essential for accurately timestamping and correlating sensor data. If synchronization packets are delayed or lost, appropriate techniques (e.g., periodic resynchronization, local clock drift correction, etc.) and robust protocols should be used to help maintain timing accuracy [84]. * The efficiency and durability of WSN coverage as well as of route optimizations are pivotal, as WSNs can be rapidly deployed across wide or inaccessible areas. The requirement of gathering data from all network sensors creates limitations on the distance between them [85]. Sensor fusion techniques are employed to combine data from diverse sensors.