• Energy Research

As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emission-efficient Application placement method (DECA) for ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with a Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the ability of DECA in providing a tradeoff and optimizing energy cost and carbon emission at the same time.

Performance and compliance objectives have become crucial for critical sites. Balancing security, energy and costs with operational objectives is the current main challenge for critical site administrators. While current systems are not integrated enough to provide an overall view of the site performances on such targets, the FUSE-IT project intend to propose a new paradigm: the convergence of monitoring on energy, building and facilities, cyber and physical security, and ICT will leverage critical sites activities and offer new opportunity in the detection of threats or savings. In this paper, we detail the first building block of the FUSE-IT concept which lies in the definition of cross-domain (i.e. on energy, security, ICT and facility) Key Performance Indicators (KPIs). These KPIs are the main decision variables for an enhanced Decision Support System (DSS) meeting the next century impediments of critical sites.

This paper investigates the fee scheduling problem of electric vehicles (EVs) at the micro-grid scale. This problem contains a set of charging stations controlled by a central aggregator. One of the main stakeholders is the operator of the charging stations, who is motivated to minimize the cost incurred by the charging stations, while the other major stakeholders are vehicle owners who are mostly interested in user convenience, as they want their EVs to be fully charged as soon as possible. A bi-objective optimization problem is formulated to jointly optimize two factors that correspond to these stakeholders. An online centralized scheduling algorithm is proposed and proven to provide a Pareto-optimal solution. Moreover, a novel low-complexity distributed algorithm is proposed to reduce both the transmission data rate and the computation complexity in the system. The algorithms are evaluated through simulation, and results reveal that the charging time in the proposed method is 30% less than that of the compared methods proposed in the literature. The data transmitted by the distributed algorithm is 33:25% lower than that of a centralized one. While the performance difference between the centralized and distributed algorithms is only 2%, the computation time shows a significant reduction. 11 pages. accepted by IEEE Transactions on Smart Grid

This paper proposes a novel way to enable sensor-to-actor communications in wireless sensor and actor networks WSANs that only requires a simple modification to the IEEE 802.15.4 header. Although this standard includes the definition of sensor-to-actor communication, it does not address how to provide it. Our proposal is able to provide this sensor-to-actor communication at the 802.15.4 MAC layer, thereby reducing the overhead of the additional network header, and achieving an important reduction of the energy consumption. In addition, avoiding the need of a network layer with full routing capabilities in the sensors provides further memory, processing and communications saving. Moreover, this paper considers two scenarios for sensor-to-actor communications: intra-cluster and inter-cluster. The former enables the communication among sensors/actors at the same wireless sensor and actor network, and the latter allows sensors/actors located in geographically separated cluster to communicate transparently as if they were in the same WSAN.

Green communications have witnessed significant attention being paid to the next generation of wireless systems research and development. This is due to growing use of sensor- and battery-oriented smart wireless devices. The related literature in green communications for next-generation wireless systems majorly relies on transmission and sensing power management, but lacks a fault-tolerant centric approach. In this context, this paper presents a fault-tolerant and reliable green communications framework for next-generation wireless systems (FRGNWS). Firstly, maximum node-disjoint routes from all source nodes to the base station are identified based on the hybrid adapted grey wolf sine cosine optimizer. Secondly, a fault-tolerant and reliable route is selected from the maximum disjoint routes for each sensor node to the base station based on the hybrid adapted grey wolf whale optimizer. The performance of our proposed green communications framework is assessed by simulation experiments considering a realistic implementation scenario and different metrics. Simulation results clearly validate the efficacy of the proposed green communications framework as compared to the state-of-the-art techniques.

In intelligent sensing systems with wireless sensor nodes, energy efficiency is one of the most important research issues. In this paper, we focus on energy efficiency for monitoring a large-scale object such as gas and chemical material diffusion and spread of radioactive contamination and wild fire. For monitoring of a large-scale object, a great number of sensor nodes might be participated in object detection and tracking. Thus, general functions of such huge quantities of sensor nodes like sensing and message exchanging could be sources of energy exhaustion and shorten network lifetime. Therefore, we firstly adopt the sleep/wakeup state switching to restrict active sensor nodes for object tracking. That is, since an object dynamically alter its own shape by wind or geographical condition, we support that only the sensor nodes around the current boundary of the changeable object actively function while the others are on the sleep mode. In addition, we also propose that active nodes are steadily held as a small set of sensor nodes collaborated for detecting and tracking of the current boundary. A variety of computational simulations proves that our proposal is able to provide high energy efficiency as well as to trace accurate boundary shapes.

Nowadays Passive Optical Network (PON) requires that Optical Network Units (ONUs) wake up periodically to check if the Optical Line Terminal (OLT) has any message directed to them. This implies that ONUs change from sleeping mode in which they just consume 1 W to active mode in which the consumption goes up to 10 W. In many cases, the OLT does not have any packets for the ONU and it goes to sleep again, what supposes a waste of energy. In this paper, we propose a novel Hybrid ONU that relies on a low-cost and low-energy technology, IEEE 802.15.4, to wake up those ONUs that are going to receive a packet. Our first estimations demonstrates that our solution would save around 25000$ per year and OLT.