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Phasor Measurement Units (PMUs) have enabled real-time power grid monitoring and control applications realizing an integrated power grid and communication system. The communication network formed by PMUs has strict latency requirements. If PMU measurements cannot reach the control centre within the latency bound, they will be invalid for calculation and may compromise the observability of the whole power grid as well as related applications. To address this issue, this study proposes a model to account for the power grid observability under communication constraints, where effective capacity is adopted to perform a cross-layer statistical analysis in the communication system. Based on this model, three algorithms are proposed for improving power grid observability, which are an observability redundancy algorithm, an observability sensitivity algorithm and an observability probability algorithm. These three algorithms aim at enhancing the power system observability via the optimal communication resource allocation for a given grid infrastructure. Case studies show that the proposed algorithms can improve the power system performance under constrained wireless communication resources.

Internet and Communication Technology/electrical and electronic equipment (ICT/EEE) form the bedrock of today’s knowledge economy. This increasingly interconnected web of products, processes, services, and infrastructure is often invisible to the user, as are the resource costs behind them. This ecosystem of machine-to-machine and cyber-physical-system technologies has a myriad of (in)direct impacts on the lithosphere, biosphere, atmosphere, and hydrosphere. As key determinants of tomorrow’s digital world, academic institutions are critical sites for exploring ways to mitigate and/or eliminate negative impacts. This Report is a self-deliberation provoked by the question How do we create more resilient and healthier computer science departments: living laboratories for teaching and learning about resource-constrained computing, computation, and communication? Our response for University College London (UCL) Computer Science is to reflect on how, when, and where resources—energy, (raw) materials including water, space, and time—are consumed by the building (place), its occupants (people), and their activities (pedagogy). This perspective and attendant first-of-its-kind assessment outlines a roadmap and proposes high-level principles to aid our efforts, describing challenges and difficulties hindering quantification of the Department’s resource footprint. Qualitatively, we find a need to rematerialise the ICT/EEE ecosystem: to reveal the full costs of the seemingly intangible information society by interrogating the entire life history of paraphernalia from smartphones through servers to underground/undersea cables; another approach is demonstrating the corporeality of commonplace phrases and Nature-inspired terms such as artificial intelligence, social media, Big Data, smart cities/farming, the Internet, the Cloud, and the Web. We sketch routes to realising three interlinked aims: cap annual power consumption and greenhouse gas emissions, become a zero waste institution, and rejuvenate and (re)integrate the natural and built environments.

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

Blockchain is one of the promising technologies nowadays due to its unique characteristics like security, privacy, data integrity, decentralization, immutability, and traceability. Originally used to implement cryptocurrencies, recently numerous applications have employed blockchain in their architectures including applications targeted for the internet of things (IoT) environments. It is expected that by 2025 more than 21 billion IoT devices will be used especially with use of cloud, fog and edge computing architectures. Integrating blockchain in the IoT architecture provides many advantages such as enhancing security and privacy, better speed and costs, traceability and reliability, and elimination of single point of failure. On the other hand, many issues and challenges have arisen and should be addressed. Typically, IoT system consists of lightweight devices with limited hardware resources and constraints. Hence, the energy efficiency is a fundamental challenge in such devices. The main motivation of this paper is to survey designing a secure and energy efficient blockchain-based IoT implementation using a suitable hardware design. The paper classifies, presents and analyzes existing solutions to better implement IoT environment combined with blockchain technology. Our investigation demonstrations that most of lightweight solutions handle either the energy or security issue separately. Moreover, many works are theoretical-based analysis and solutions without considering the real blockchain-based IoT validation design. Energy evaluation for IoT hardware devices is not given the adequate research bandwidth. Additionally, limited works evaluated their techniques from hardware constrained device perspective. It is recommended that the performance of any proposed solution should be validated using real designs. The hardware perspective evaluation should be in mind for efficient blockchain-based IoT hardware implementation. The proposed lightweight solutions should focus more on efficient energy implementation while considering the lightweight security mechanisms.

This study addresses the multi-objective multi-mode resource-constrained project scheduling problem with payment planning where the activities can be done through one of the possible modes and the objectives are to maximize the net present value and minimize the completion time concurrently. Moreover, renewable resources including manpower, machinery, and equipment as well as non-renewable ones such as consumable resources and budget are considered to make the model closer to the real-world. To this end, a non-linear programming model is proposed to formulate the problem based on the suggested assumptions. To validate the model, several random instances are designed and solved by GAMS-BARON solver applying the ε-constraint method. For the high NP-hardness of the problem, we develop two metaheuristics of non-dominated sorting genetic algorithm II and multi-objective simulated annealing algorithm to solve the problem. Finally, the performances of the proposed solution techniques are evaluated using some well-known efficient criteria.

The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot be achieved with resource-constrained edge computing. This study presents a novel approach and a proof of concept for a hardware-efficient automated license plate recognition system for a constrained environment with limited resources. The proposed solution is purely implemented for low-resource edge devices and performed well for extreme illumination changes such as day and nighttime. The generalisability of the proposed models has been achieved using a novel set of neural networks for different hardware configurations based on the computational capabilities and low cost. The accuracy, energy efficiency, communication, and computational latency of the proposed models are validated using different license plate datasets in the daytime and nighttime and in real time. Meanwhile, the results obtained from the proposed study have shown competitive performance to the state-of-the-art server-grade hardware solutions as well.

In 5G network slicing environments, if insufficient resources are allocated to the associated resource-intensive service slices, its quality of service (QoS) can be considerably degraded. In this paper, we propose a priority-based dynamic resource allocation scheme (PDRAS), in which a resource management agent maintains slicing information such as priorities, demand profiles, and average resource adjustment time to change allocated resources to slices. To maximize QoS of slices while maintaining the total amount of allocated resources below a certain level, a constrained Markov decision process problem is formulated and the optimal allocation policy is obtained using linear programming. Extensive evaluation results demonstrate that PDRAS with the optimal policy has better performance regarding QoS and the resource usage efficiency compared with other schemes.

The concept of the Internet of Things (IoT) arises due to the change in the characteristics and numbers of smart devices. Communication of things makes it important to ensure security in this interactive architecture. One of the developments that are subject to change in IoT environments is post-quantum cryptography. This evolution, which includes the change of asymmetric cryptosystems, affects the security of IoT devices. In this paper, fundamental characteristics and layered architecture of IoT environments are examined. Basic security requirements and solution technologies for IoT architecture are remembered. Some important open problems in the literature for IoT device security are recalled. From these open problems, the post-quantum security of IoT devices with limited resources is focused. The main purpose of this paper is to improve the constrained resource classification and give a point of view for post-quantum IoT security. In this context, a sensitive classification is proposed by improving the limited resource classification of IETF. The cryptosystem efficiency definition is made for the analysis of resource-constrained device security. Using the proposed classification and efficiency definition, the usage of lattice-based cryptosystems in resource-constrained IoT device security is analyzed.

The existence of quantum computers and Shor’s algorithm poses an imminent threat to classical public-key cryptosystems. These cryptosystems are currently used for the exchange of keys between servers and clients over the Internet. The Internet of Things (IoT) is the next step in the evolution of the Internet, and it involves the connection of millions of low-powered and resource-constrained devices to the network. Because quantum computers are becoming more capable, the creation of a new cryptographic standard that cannot be compromised by them is indispensable. There are several current proposals of quantum-resistant or post-quantum algorithms that are being considered for future standards. Given that the IoT is increasing in popularity, and given its resource-constrained nature, it is worth adapting those new standards to IoT devices. In this work, we study some post-quantum cryptosystems that could be suitable for IoT devices, adapting them to work with current cryptography and communication software, and conduct a performance measurement on them, obtaining guidelines for selecting the best for different applications in resource-constrained hardware. Our results show that many of these algorithms can be efficiently executed in current IoT hardware, providing adequate protection from the attacks that quantum computers will eventually be capable of.

Blockchain is among the most promising new technologies due to its unique features, encompassing security, privacy, data integrity, and immutability. Blockchain applications include cryptocurrencies such as Bitcoin. Recently, many other applications have begun to deploy blockchain in their systems. These applications include internet of things (IoT) environments. Although deploying blockchain in IoT architecture has yielded numerous advantages, issues and challenges have arisen that require further research. Most IoT devices and platforms have limited storage capacity, low battery power, and limited hardware resources for computation and network communication. Thus, energy efficiency is a critical factor in these devices. On the other hand, blockchain requires extensive resources and high computational capabilities for mining and communication processes. Balancing computation complexity and IoT resources is a fundamental design challenge in implementing blockchain functions, including the hash function, which is crucial to blockchain design for the mining process. In this study, we present a literature review on the common hash functions used in blockchain-based applications, in addition to the lightweight hash functions available in literature. We evaluate and test the common lightweight hash functions (SPONGENT, PHOTON, and QUARK) on FPGA platforms to determine which is most suitable for blockchain-IoT devices. Moreover, we assess lightweight hash functions in terms of area, power, energy, security, and throughput. The results show tradeoffs between these hash functions. SPONGENT performs best on security and throughput. QUARK consumes the least power and energy but has the lowest security parameters. PHOTON utilizes less area and offers a balance between multiple performance metrics (area, energy, and security), rendering it the most suitable lightweight hash function.