The dynamic resource allocation is a good feature of the cloud computing environment. However, it faces serious problems in terms of service quality, fault tolerance, and energy consumption. It was necessary, then, to find an effective method that can effectively address these important issues and increase cloud performance. This paper presents a dynamic resource allocation model that can meet customer demand for resources with improved and faster responsiveness. It also proposes a multi-objective search algorithm called Spacing Multi-Objective Antlion algorithm (S-MOAL) to minimize both the makespan and the cost of using virtual machines. In addition, its impact on fault tolerance and energy consumption was studied. The simulation revealed that our method performed better than the PBACO, DCLCA, DSOS and MOGA algorithms, especially in terms of makespan.

The Ontology for Robotics and Automation (ORA) group was formed to develop a standard ontology specifying the most general concepts and relations in robotics and automation (R&A). The result of its work has been published as the IEEE standard 1872-2015. Its main component is CORA, a core ontology that extends the top-ontology Suggested Upper Merged Ontology (SUMO) with concepts such as robot, robot group, robotic systems and robot interaction. CORA serves as basis for creating more specific domains ontologies for subfields of robotics. It can be used as a basis for the construction of a robot/human-robot communication system, where the robot can represent itself, other agents and the environment. Different groups have been working in specializing the main standard with additional knowledge structures referring to other areas of R&A. In this chapter, we navigate among the concepts and processes used during the development of CORA, showing how it is being used and how it is currently being developed for human-robot interaction.

Cloud computing is increasingly recognized as a new way to use on-demand, computing, storage and network services in a transparent and efficient way.The development of applications in cloud environments is faced with the need to efficiently schedule a large number of tasks and resources.However, in the most of the time, the resources in cloud are not efficiently utilized due to inadequate scheduling task algorithm in virtual machines.Therefore, task scheduling is one of the most challenging issues in cloud computing.In this paper, we propose two-objective virus optimization algorithm of the makespan and the cost, for mapping tasks to virtual machines in order to meet the needs of cloud service quality and proper assignment of resources.Thus, based on genetic algorithm some parameters of Virus optimization algorithm are redefined to strengthen sorting ability between virus infection strategies.Our combined methods aims to improve the performance of scheduling algorithms.It outperforms some existing approaches for task scheduling in Cloud computing.

The paradigm of Internet of Robotic Things (IoRT) extends the scope of the Internet of Things by endowing any object with the three main typical functions of any robotic system: perception, actuation, and control. This paper presents a semantic framework for context-aware IoRT systems to support the development of applications for monitoring and managing IoRT systems. A knowledge representation framework, called SmartRules, is proposed for context modeling. SmartRules is a production rules language that enables reactive reasoning based on the closed world and unique name assumptions. It allows producing actions based on contextual information represented in a dedicated ontology language, called μ-Concept. An operational platform, centered on the notion of manageable object (MO), is also proposed to abstract the access to any physical or virtual device, which can communicate through the Internet. In addition, an integrated methodology and tools are proposed for guiding the development and deployment of context-aware Semantic IoRT systems, and, in particular, for defining context semantics and creating context management rules. To show the effectiveness of the proposed framework in ambient assisted living (AAL) applications, an IoRT system dedicated to the monitoring and assistance of an elderly person during his/her daily living activities is described and evaluated.

Building context-aware pervasive computing systems - such as ambient intelligent spaces or ubiquitous robots needs to take into account the quality of contextual information collected from sensors. Such information are often inaccurate, uncertain or subject to noise due to environment and user dynamics. Dempster-Shafer theory has been extensively adopted to handle uncertainty in situation and activity recognition. This theory is used to represent, manipulate and decide under uncertainty. However, combining information using Dempster's rule may produce counterintuitive decision in highly conflicting evidences due to sources failure. Recently, a variety of rules were proposed to overcome such drawback. Inspired by Murphy's rule, we propose in this paper a new rule called "Weighted Average Combination Rule" (WACR) to deal with context recognition in highly dynamic environment such as ambient intelligence spaces. The proposed WACR rule is based on evidence arithmetic average and cardinality. WACR rule was applied to some conflictual evidence examples and has been shown to reap more appropriate decisions than other alternative rules for decision-making in activity-aware systems. To demonstrate the applicability and performance of our approach, we have studied a scenario of context recognition in an ambient intelligent environment. In this scenario, we simulated a smart kitchen composed of status devices and RFID sensors that allow determining what is the artifact in use by the inhabitant and for which activity.

In the present paper, a method based on a new concept called power fuzzy soft set is proposed for multi-observer decision making problems under uncertain information. The new method applies a weighted conjunctive operator to aggregate these sets into a reliable resultant power fuzzy soft set from the input data set. To decide among the alternatives, a new ranking algorithm is introduced. The effectiveness and feasibility of this method are demonstrated by comparing it to algorithms based on the maximum score in decision making.

This paper presents a knowledge-based engineering framework for the design, deployment and running of context-aware monitoring agents that are dedicated to ambient assisted living applications. A new modeling approach of agent knowledge, that combines the advantages of both ontologies and object oriented modeling and programming, is proposed. In this approach, the agents' logic is implemented using a micro-ontology and production rules based on the closed world assumption, called smart rules. These rules are managed using a standard reasoning system embedded in the agent core. Unlike semantic web approaches, the proposed approach rely on the closed world and unique name assumptions. These features are required for monitoring purposes in ambient intelligence and robotics domains. We present a practical work, where monitoring agents are instantiated in the user environment and their reasoning rules operate to handle the detection and confirmation of abnormal and emergency situations with respect to user's context. These rules allow the agents to trigger appropriate actions with help of companion robot.

In recent years, a plethora of different studies for design of traditional ensemble classifiers has been proposed in order to improve final recognition accuracy. However, among the ensemble classifiers, combination methods are focused on building independent classifiers of the same or different algorithms using majority voting methods. In this paper, we present a new fusion scheme for ensemble classifiers based on a new concept called Generalized Fuzzy Soft Set (GFSS), which we apply in activity classification. Essentially, we apply a weighted aggregate operator to the output of each classifier in order to fuse the GFSS into a more reliable classifier. The proposed fusion method is based on a new ranking algorithm to classify activities. We show that the proposed method produces more accurate results than the best single classifier and its effectiveness is demonstrated by comparing it with single classifier in terms of activity recognition accuracy.

In this chapter we discuss the necessity to move beyond built-in monotonic semantic web based reasoning-architectures for endowing ubiquitous robots with cognitive capabilities, which are strongly required in ambient assistive living, towards new architectures that combine different reasoning mechanisms to achieve better context awareness and adaptability in dynamic environments. We also present practical reasoning approaches that we have developed during the last decade for ambient intelligence and robotics applications. Finally, we discuss future directions that should be investigated to implement high-level cognitive capabilities that can be supported by cloud computing platforms as reasoning backend for robots and connected devices in smart spaces. These will enhance the human-environment interaction using robots, emergency prevention, management and rescue.

We propose three mechanisms to manage nodes energy and improve the efficiency of real-time routing protocols in sensor networks. To preserve nodes' resources and to improve network fluidity, the first mechanism removes each useless packet due to its insufficient deadline in reaching the sink. To reinforce the packet real-time aspect, the second mechanism selects from the current-node queue the most urgent packet to be forwarded first. For a better node energy balancing, the third mechanism uses both the residual energy and the relay speed of the forwarding candidate neighbour to select the next forwarder of the current packet. These mechanisms are simple to implement, require very little states and rely only on local primitives. In addition they can be easily integrated in any geographic routing protocol. Associated with the real-time routing protocol SPEED in TinyOS and evaluated in the simulator TOSSIM, our proposals achieved good performance in terms of node energy balancing, packet loss ratio and energy consumption.

In order to have a normal behavior in combination of bodies of evidence, this paper proposes a new combination rule. This rule includes the cardinality of focal set elements in conjunctive operation and the conflict redistribution to all steps. Based on the focal set cardinalities, the conflict redistribution is assigned using factors. The weighted factors are computed from the original and the conjunctive masses assigned to each focal element. This strategy forces the conflict redistribution in favor of the more committed hypothesis. Our method is evaluated and compared with some numerical examples reported in the literature. As result, this rule redistributes the conflict in favor of the more committed hypothesis and gives intuitive interpretation for combining multiple information sources with coherent results.

These last years, a lot of combination rules emerged in order to model the situations of belief fusion. These rules can be classified in two different classes. However, these rules do not differentiate between focal elements in the combination step which produce counterintuitive results in some situations. Motivated by this observation, we propose a new combination rule which hybrids the strategies of these two classes. Our rule is two-step operator where the averaging step comes first, and then the conflict redistribution step. Experimental studies are conducted on a real smart home dataset to show the accuracy of our rule in ubiquitous-assisted living situation.

We propose two simple mechanisms that aim at improving the energy efficiency of SPEED; the well-known real-time routing protocol dedicated for wireless sensor networks (WSNs). The first mechanism calculates the expected end-to-end delay on the basis of previous hops’ delay and drops a packet if such expected delay is greater than the packet deadline. By dropping the useless delayed packets, the mechanism increases fluidity of links and economises energy of nodes. The second mechanism extends the component stateless non-deterministic geographic forwarding (SNGF) of the SPEED protocol. While the original SNGF performs energy balancing by randomly selecting the next hop from the forwarding candidate neighbours set (FS), the proposed mechanism makes use of a decision parameter which takes into account both the relay speed and the residual energy of the candidates. Thus, it prolong the network lifetime without degrading the real-time packet delivery performance. Also, it uses a low-cost algorithm updating continually the residual energy of neighbours in a node by exploiting both the overhearing mechanism and the location beaconing which are implemented in the SPEED protocol. Associated with SPEED, the proposed mechanisms achieved good performance in terms of node energy balancing and network energy consumption without degrading the packet delivery ratio.

The geographical routing suffers from communication voids in wireless sensor networks WSNs. Thus, several void-handling techniques are proposed in the literature, but they are limited in case of critical applications. In this paper, we propose an efficient approach handling both open and closed voids by using three complementary mechanisms. The first mechanism orients each packet that arrives on the network boundary toward the network middle by using the shortest path leading to the sink. The second mechanism is used by a node located on the boundary of a closed void to orient the received packets in optimal paths toward the sink. The third mechanism uses repellent forces generated by each closed void to repulse the packets from its boundary. To discharge the boundary nodes, our approach defines an announcement zone around each closed void and uses a two-hop forwarding mode in each boundary node. Our proposals achieved good performance.

Closed voids are created within a deployed wireless sensor network (WSN), but open voids are often formed on the boundary of this network. Geographical routing protocols must handle these voids where packets fall into local minima. To contribute on resolving this problem, we propose in this paper an effective void-tolerant routing approach based on two mechanisms for handling any kind of void. Our approach uses simple and effective algorithms ensuring discovery and maintenance of voids in a WSN. Contrary to existing void-handling techniques, our proposal uses information of all voids for better orienting data packets toward their destination in optimal paths around the voids. Proposed approach has good performances in terms of packet delivery ratio, average routing path length, control packets overhead, energy of network and boundary nodes consumed per delivered packet, and average residual deadline of delivered packets.

Scalable geographical routing protocols suffer from voids that appear in Wireless Sensor Networks (WSNs). Several techniques are proposed in literature to handle this problem, but they present some limits, particularly in time-critical applications. Consequently, we propose in this paper a new 2-hop forwarding approach that orients any packet which arrives at a boundary node in the shortest path towards the sink. The handled voids can be either closed within a deployed WSN or open on the network boundary. To keep unchanged the actual size of a void for a long time, the use of a 2-hop forwarding mode is privileged to preserve the limited energy of boundary nodes. The information needed for our approach is provided by simple and reactive algorithms that we propose in this paper to discover and maintain the boundaries of voids. Associated with the SPEED real-time routing protocol, our proposal performs very well in terms of packet delivery ratio, control packet overhead, network and boundary nodes energy consumption.

Open voids are often formed on the boundary of a deployed wireless sensor network (WSN). Geographical routing protocols must handle these voids where packets fall into local minima. To contribute on resolving this problem, we propose in this paper an effective mechanism for handling this kind of voids. It uses two simple and effective algorithms ensuring discovery and maintenance of the network boundary. Contrary to existing void-handling techniques, our proposal uses the information about this boundary and the destination node for better directing data packets in optimal paths. Thus, open voids are avoided with great efficiency. The proposed mechanism has good performances in terms of packet delivery ratio, average routing path length, boundary energy consumed per delivered packet and average residual deadline of all delivered packets.