Biblio

Time and cost are the most critical performance metrics for computer systems including embedded system, especially for the battery-based embedded systems, such as PC, mainframe computer, and smart phone. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures this paper proposes an optimal energy efficiency and system performance model and the OTHAP (Optimizing Task Heterogeneous Assignment with Probability) algorithm to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with areal-time embedded systems guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task and The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).

Underwater acoustic networks is an enabling technology for a range of applications such as mine countermeasures, intelligence and reconnaissance. Common for these applications is a need for robust information distribution while minimizing energy consumption. In terrestrial wireless networks topology information is often used to enhance the efficiency of routing, in terms of higher capacity and less overhead. In this paper we asses the effects of topology information on routing in underwater acoustic networks. More specifically, the interplay between long propagation delays, contention-based channels access and dissemination of varying degrees of topology information is investigated. The study is based on network simulations of a number of network protocols that make use of varying amounts of topology information. The results indicate that, in the considered scenario, relying on local topology information to reduce retransmissions may have adverse effects on the reliability. The difficult channel conditions and the contention-based channels access methods create a need for an increased amount of diversity, i.e., more retransmissions. In the scenario considered, an opportunistic flooding approach is a better, both in terms of robustness and energy consumption.

Honeynet is deployed to trap attackers and learn their behavior patterns and motivations. Conventional honeynet is implemented by dedicated hardware and software. It suffers from inflexibility, high CAPEX and OPEX. There have been several virtualized honeynet architectures to solve those problems. But they lack a standard operating environment and common architecture for dynamic scheduling and adaptive resource allocation. Software Defined Security (SDS) framework has a centralized control mechanism and intelligent decision making ability for different security functions. In this paper, we present a new intelligent honeynet architecture based on SDS framework. It implements security functions over Network Function Virtualization Infrastructure (NFVI). Under uniform and intelligent control, security functional modules can be dynamically deployed and collaborated to complete different tasks. It migrates resources according to the workloads of each honeypot and power off unused modules. Simulation results show that intelligent honeynet has a better performance in conserving resources and reducing energy consumption. The new architecture can fit the needs of future honeynet development and deployment.

This research paper identifies security issues; especially energy based security attacks and enhances security of the system. It is very essential to consider Security of the system to be developed in the initial Phases of the software Cycle of Software Development (SDLC) as many billions of bucks are drained owing to security flaws in software caused due to improper or no security process. Security breaches that occur on software system are in umpteen numbers. Scientific Literature propose many solutions to overcome security issues, all security mechanisms are reactive in nature. In this paper new security solution is proposed that is proactive in nature especially for energy based denial of service attacks which is frequent in the recent past. Proposed solution is based on energy consumption by system known as energy points.

This paper considers the physical layer security for the cluster-based cooperative wireless sensor networks (WSNs), where each node is equipped with a single antenna and sensor nodes cooperate at each cluster of the network to form a virtual multi-input multi-output (MIMO) communication architecture. We propose a joint cooperative beamforming and jamming scheme to enhance the security of the WSNs where a part of sensor nodes in Alice's cluster are deployed to transmit beamforming signals to Bob while a part of sensor nodes in Bob's cluster are utilized to jam Eve with artificial noise. The optimization of beamforming and jamming vectors to minimize total energy consumption satisfying the quality-of-service (QoS) constraints is a NP-hard problem. Fortunately, through reformulation, the problem is proved to be a quadratically constrained quadratic problem (QCQP) which can be solved by solving constraint integer programs (SCIP) algorithm. Finally, we give the simulation results of our proposed scheme.

The main challenge of ultra-reliable machine-to-machine (M2M) control applications is to meet the stringent timing and reliability requirements of control systems, despite the adverse properties of wireless communication for delay and packet errors, and limited battery resources of the sensor nodes. Since the transmission delay and energy consumption of a sensor node are determined by the transmission power and rate of that sensor node and the concurrently transmitting nodes, the transmission schedule should be optimized jointly with the transmission power and rate of the sensor nodes. Previously, it has been shown that the optimization of power control and rate adaptation for each node subset can be separately formulated, solved and then used in the scheduling algorithm in the optimal solution of the joint optimization of power control, rate adaptation and scheduling problem. However, the power control and rate adaptation problem has been only formulated and solved for continuous rate transmission model, in which Shannon's capacity formulation for an Additive White Gaussian Noise (AWGN) wireless channel is used in the calculation of the maximum achievable rate as a function of Signal-to-Interference-plus-Noise Ratio (SINR). In this paper, we formulate the power control and rate adaptation problem with the objective of minimizing the time required for the concurrent transmission of a set of sensor nodes while satisfying their transmission delay, reliability and energy consumption requirements based on the more realistic discrete rate transmission model, in which only a finite set of transmit rates are supported. We propose a polynomial time algorithm to solve this problem and prove the optimality of the proposed algorithm. We then combine it with the previously proposed scheduling algorithms and demonstrate its close to optimal performance via extensive simulations.

Because of poor performance of heuristic algorithms on virtual machine placement problem in cloud environments, a multi-objective constraint optimization model of virtual machine placement is presented, which taking energy consumption and resource wastage as the objective. We solve the model based on the proposed discrete firefly algorithm. It takes firefly's location as the placement result, brightness as the objective value. Its movement strategy makes darker fireflies move to brighter fireflies in solution space. The continuous position after movement is discretized by the proposed discrete strategy. In order to speed up the search for solution, the local search mechanism for the optimal solution is introduced. The experimental results in OpenStack cloud platform show that the proposed algorithm makes less energy consumption and resource wastage compared with other algorithms.

Wireless Sensor Networks (WSN) is vulnerable to node capture attacks in which an attacker can capture one or more sensor nodes and reveal all stored security information which enables him to compromise a part of the WSN communications. Due to large number of sensor nodes and lack of information about deployment and hardware capabilities of sensor node, key management in wireless sensor networks has become a complex task. Limited memory resources and energy constraints are the other issues of key management in WSN. Hence an efficient key management scheme is necessary which reduces the impact of node capture attacks and consume less energy. By simulation results, we show that our proposed technique efficiently increases packet delivery ratio with reduced energy consumption.

Key management is the core to ensure the communication security of wireless sensor network. How to establish efficient key management in wireless sensor networks (WSN) is a challenging problem for the constrained energy, memory, and computational capabilities of the sensor nodes. Previous research on sensor network security mainly considers homogeneous sensor networks with symmetric key cryptography. Recent researches have shown that using asymmetric key cryptography in heterogeneous sensor networks (HSN) can improve network performance, such as connectivity, resilience, etc. Considering the advantages and disadvantages of symmetric key cryptography and asymmetric key cryptography, the paper propose an efficient and hybrid key management method for heterogeneous wireless sensor network, cluster heads and base stations use public key encryption method based on elliptic curve cryptography (ECC), while using symmetric encryption method between adjacent nodes in the cluster. The analysis and simulation results show that the proposed key management method can provide better security, prefect scalability and connectivity with saving on storage space.

This article is a summary description of the Cognitive Packet Network (CPN) which is an example both of a completely software defined network (SDN) and of a self-aware computer network (SAN) which has been completely implemented and used in numerous experiments. CPN is able to observe its own internal performance as well as the interfaces of the external systems that it interacts with, in order to modify its behaviour so as to adaptively achieve objectives, such as discovering services for its users, improving their Quality of Service (QoS), reduce its own energy consumption, compensate for components which fail or malfunction, detect and react to intrusions, and defend itself against attacks.
 

Cell discontinuous transmission (DTX) is a new feature that enables sleep mode operations at base station (BS) side during the transmission time intervals when there is no traffic. In this letter, we analyze the maximum achievable energy saving of the cell DTX. We incorporate the cell DTX with a clean-slate network deployment and obtain optimal BS density for lowest energy consumption satisfying a certain quality of service requirement considering daily traffic variation. The numerical result indicates that the fast traffic adaptation capability of cell DTX favors dense network deployment with lightly loaded cells, which brings about considerable improvement in energy saving.
 

Demand response (DR), which is the action voluntarily taken by a consumer to adjust amount or timing of its energy consumption, has an important role in improving energy efficiency. With DR, we can shift electrical load from peak demand time to other periods based on changes in price signal. At residential level, automated energy management systems (EMS) have been developed to assist users in responding to price changes in dynamic pricing systems. In this paper, a new intelligent EMS (iEMS) in a smart house is presented. It consists of two parts: a fuzzy subsystem and an intelligent lookup table. The fuzzy subsystem is based on its fuzzy rules and inputs that produce the proper output for the intelligent lookup table. The second part, whose core is a new model of an associative neural network, is able to map inputs to desired outputs. The structure of the associative neural network is presented and discussed. The intelligent lookup table takes three types of inputs that come from the fuzzy subsystem, outside sensors, and feedback outputs. Whatever is trained in this lookup table are different scenarios in different conditions. This system is able to find the best energy-efficiency scenario in different situations.

Wireless Sensor Network has a wide range of applications including environmental monitoring and data gathering in hostile environments. This kind of network is easily leaned to different external and internal attacks because of its open nature. Sink node is a receiving and collection point that gathers data from the sensor nodes present in the network. Thus, it forms bridge between sensors and the user. A complete sensor network can be made useless if this sink node is attacked. To ensure continuous usage, it is very important to preserve the location privacy of sink nodes. A very good approach for securing location privacy of sink node is proposed in this paper. The proposed scheme tries to modify the traditional Blast technique by adding shortest path algorithm and an efficient clustering mechanism in the network and tries to minimize the energy consumption and packet delay.