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Underactuated AUV (automatic underwater vehicle) possess a lower number of actuators than its degrees of freedom, which helps to conserve energy and improve maneuverability in complex underwater environments. However, the complex and variable disturbance environment under the sea makes the control of underactuated AUVs extremely difficult, so it is urgent to study controllers with strong disturbance capability. This paper focuses on 3D trajectory tracking under strong disturbance environment. The 5-DOF underactuated AUV kinematics and dynamics equations are established and simplified by adopting appropriate assumptions. The NDOB (nonlinear disturbance observer) combined with sliding mode control is utilized to complete the 3D trajectory tracking of underwater unmanned cluster under strong disturbance environment. The effectiveness of the control algorithm is verified through multiple sets of simulation experiments. Through the comparison of experimental data, the anti-disturbance ability of the algorithm proposed in this paper is more than 80% higher than that of the adaptive sliding mode controller under strong disturbance environment. This research provides an effective solution for 3D Trajectory tracking of underactuated AUV, which is of great practical significance for improving the efficiency of underwater exploration, ocean observation, and underwater operations. [ABSTRACT FROM AUTHOR]

Networking with Unmanned Aerial Vehicles (UAVs) is intricate in terms of mobility management, routing conditions and timing control as high dynamics cause an immense impact on the connectivity and operations. Thus, it is consequential to formalize an efficient solution which can provide sustainable connectivity in drone networks with efficient control as well as provisioning of the high quality of service. To satisfy such requirements, this paper proposes a novel strategy by utilizing the properties of the fire-fly optimization algorithm. The proposed approach forms an efficient timing slot mechanism which results in the formation of an energy-efficient and location-aware Medium Access Control (MAC) mechanism for quality of service enhancement in UAV networks. The simulation results and comparative analysis suggest that the proposed approach is capable of providing congestion-free, collision-free and energy efficient MAC with signaling quality amended up to 59.04% along with 80.1% conservation of energy. [ABSTRACT FROM AUTHOR]

Abstract The dynamic nature of Mobile Ad hoc Networks (MANET) poses many challenges including effective management of scarce resources such as energy. Overall energy consumption of a network depends upon the transmission power of its constituent nodes and decides network lifetime. In this paper, a clustering based scalable topology control method, Optimized Power Control (OPC) is proposed to determine the optimal transmission power of nodes with the help of modified Gabriel Graph (GG) algorithm. OPC is tested for its efficiency in terms of various network parameters such as transmission power, per node energy consumption and network delay. Simulation results show that the proposed scheme is scalable and outperforms the existing schemes such as Local Tree based Reliable Topology (LTRT) and Distributed Power Management (DISPOW) in terms of the above-mentioned network parameters. Additionally, per node power level decision using OPC has been improved by approximately 7% in comparison to LTRT and DISPOW. [ABSTRACT FROM AUTHOR]

In recent decades, cluster-based schemes have emerged as viable solutions for energy conservation problem in wireless sensor networks (WSN). However, most of the existing solutions incur imbalanced energy consumption and high network overheads. In addition, existing cluster-based solutions do not optimize route discovery based on the restricted resources of sensor nodes. Moreover, most of the cluster-based solutions perform periodical re-clustering for load balancing, which results in shortening network lifetime. This research paper presents a Dynamic Energy-aware Fault Tolerant Routing (DEFTR) protocol that exploits uniform-sized network partitioning based on network size and utilizes a multi-facet routing mechanism, which takes into consideration the residual energy, and position and link quality of neighbors. Furthermore, DEFTR not only offers reliable and energy efficient data routing but also supports fault tolerance. Simulation results demonstrate that DEFTR improved the network lifetime by 20.9% and throughput by 35%, also it reduced the delay by 29% and transmission cost by 46% in comparison to the existing work. [ABSTRACT FROM AUTHOR]

In this paper, we optimize the energy consumption and node placements of a wireless sensor network (WSN) using Simulated Annealing (SA). We propose an energy-aware optimization of the power control, the packet transmission and the topology control in WSNs. An optimization problem of minimizing the energy consumption, which incorporates the power control, the packet transmission through error correcting codes (ECC) and the topology control fulfilling specific coverage constraints, is formulated. The considered joint optimization problem is proved NP-complete and for that reason a heuristic method based on SA is applied. By applying SA algorithm, an updated topology is evaluated at any convergence point and thus a near-optimal transmit power and packet length, in conjunction with node placements is achieved jointly. Our results confirm that the proposed near optimal topology deployment proposed by SA leads to greater network coverage and lower energy consumption as compared to a Random Sampling (RS) heuristic. [ABSTRACT FROM AUTHOR]

A Mobile Ad hoc Network (MANET) is an infrastructure-less collection of nodes that are powered by portable batteries. Consumption of energy is the major constraint in a wireless network. This paper presents a new algorithm called Energy-Aware Span Routing Protocol (EASRP) that uses energy-saving approaches such as Span and the Adaptive Fidelity Energy Conservation Algorithm (AFECA). Energy consumption is further optimized by using a hardware circuit called the Remote Activated Switch (RAS) to wake up sleeping nodes. These energy-saving approaches are well-established in reactive protocols. However, there are certain issues to be addressed when using EASRP in a hybrid protocol, especially a proactive protocol. Simulation results for the EASRP protocol show an increase in energy efficiency of 12.2% and 17.45% compared with EAZRP and ZRP, respectively. The EASRP protocol also proves to be effective in by producing a better packet delivery ratio for low- and high-density networks as measured by the NS-2 simulation tool. [ABSTRACT FROM AUTHOR]

The world of home automation is an exciting field that has exploded with new technologies and today is known as an area where “The internet of things” (IoT) vision becomes reality. The primary advantages that stem from this concept include how each device forms a small part of the Internet, by which the advanced system is able to interact and communicate, maximizes safety, security, comfort, convenience and energy-savings. This paper proposes an implementation of Sensor Web node as a part of IoT using a Raspberry Pi – inexpensive, fully customizable and programmable small computer with support for a large number of peripherals and network communication. Using this technology, in an example of monitoring and determining the confidence of fire in building, a full system, based on Sensor Web elements, is created and developed starting from a scratch. The given example confirms the advantage of Raspberry Pi – flexibility and extensive possibility of its usage. [ABSTRACT FROM AUTHOR]

Key distribution is required to secure e-health applications in the context of Internet of Things (IoT). However, resources constraints in IoT make these applications unable to run existing key management protocols. In this paper, we propose a new lightweight key management protocol. This protocol is based on collaboration to establish a secure end-to-end communication channel between a highly resource constrained node and a remote entity. The secure channel allows the constrained node to transmit captured data while ensuring confidentiality and authentication. To achieve this goal, we propose offloading highly consuming cryptographic primitives to third parties. As a result, the constrained node obtains assistance from powerful entities. To assess our protocol, we conduct a formal validation regarding security properties. In addition, we evaluate both communication and computational costs to highlight energy savings. The results show that our protocol provides a considerable gain in energy while its security properties are ensured. [ABSTRACT FROM AUTHOR]

In cognitive radio networks, it is well known that the cooperative spectrum sensing can overcome damaging effects of fading and shadowing. However, it also increases the amount of energy consumption which is a critical factor in low powered wireless communications. In this paper based on bi-threshold energy detection, we maximize the network throughput such that the energy consumption is below a predefined value and also sufficient protection of primary users against interference is guaranteed. Convex optimization analysis is presented to jointly obtain the optimal values of sensing time and detection thresholds. Simulation results show that the proposed method is very flexible such that a good tradeoff between achievable throughput and energy efficiency can be established, while it often outperforms the conventional sensing method significantly. [ABSTRACT FROM AUTHOR]

The necessity and significance of improving the energy efficiency of cloud implementations have increased due to the rapid growth and proliferation of cloud computing services around the world. Virtual machines (VMs) comprise the backend of most, if not all, cloud computing services. Several VMs are often consolidated on a physical machine to efficiently utilize its resources. In this paper, we take into account the cooling and network structure of the datacenter host ing the physical machines when consolidating the VMs so that fewer racks and routers are employed, without compromising the service-level agreements; consequently, idle routing and cooling equipment can be turned off in order to reduce the energy consumption. Our experimental results on four benchmarks show that the proposed techniques improve energy consumption of servers, network equipment, and cooling systems by 2.5%, 18.8%, and 28.2% respectively compared to state of the art, resulting in a total of 14.7% energy improvement on average in the entire datacenter. [ABSTRACT FROM AUTHOR]

Forecasting electricity consumption can help policymakers to properly plan for economic development. This is possible through energy conservation by avoiding excessive consumption of electricity through enhanced operational strategy. Power utilization and financial improvement are in long term relationship with all member nations of the Organization of Petroleum Exporting Countries (OPEC). In order to improve electricity consumption forecasting performance, this paper proposes an alternate machine learning method for forecasting OPEC electricity consumption with improved performance. The modeling of the OPEC electricity utilization forecast depends on the Cuckoo Search Algorithm by means of Lévy flights. The proposed method is found to be efficient, operative, consistent, and robust compared to the electricity consumption forecasting methods that have already been discussed by researchers in the literature. In turn, energy conservation can be motivated in the twelve OPEC member countries. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2008.06.008 Byline: Radha Guha, Nader Bagherzadeh, Pai Chou Keywords: HW/SW co-design; Streaming applications; Application convergence; Hardware resource management; Task placement; Partitioning and scheduling algorithm Abstract: There are many design challenges in the hardware-software co-design approach for performance improvement of data-intensive streaming applications with a general-purpose microprocessor and a hardware accelerator. These design challenges are mainly to prevent hardware area fragmentation to increase resource utilization, to reduce hardware reconfiguration cost and to partition and schedule the tasks between the microprocessor and the hardware accelerator efficiently for performance improvement and power savings of the applications. In this paper a modular and block based hardware configuration architecture named memory-aware run-time reconfigurable embedded system (MARTRES) is proposed for efficient resource management and performance improvement of streaming applications. Subsequently we design a task placement algorithm named hierarchical best fit ascending (HBFA) algorithm to prove that MARTRES configuration architecture is very efficient in increased resource utilization and flexible in task mapping and power savings. The time complexity of HBFA algorithm is reduced to O(n) compared to traditional Best Fit (BF) algorithm's time complexity of O(n.sup.2), when the quality of the placement solution by HBFA is better than that of BF algorithm. Finally we design an efficient task partitioning and scheduling algorithm named balanced partitioned and placement-aware partitioning and scheduling algorithm (BPASA). In BPASA we exploit the temporal parallelism in streaming applications to reduce reconfiguration cost of the hardware, while keeping in mind the required throughput of the output data. We balance the exploitation of spatial parallelism and temporal parallelism in streaming applications by considering the reconfiguration cost vs. the data transfer cost. The scheduler refers to the HBFA placement algorithm to check whether contiguous area on FPGA is available before scheduling the task for HW or for SW. Author Affiliation: EECS Department of UC, Irvine, United States