Crucial Problem Of Minimization Of Energy Computer Science

Essay add: 11-01-2017, 18:46   /   Views: 21

Xinbo Gao presented network coding as an effective strategy for improving the performance of wireless networks. However, most existing work studied network coding based on fixed route. Meanwhile, due to the limitation of the energy capacity of wireless networks, prolonging the network lifetime is a crucial task. For this purpose, this paper introduces an energy-efficient coding-aware routing (EECAR) mechanism, which combines the network coding with energy efficiency. EECAR can detect potential coding opportunities under "two-hop" coding conditions and prolong the network lifetime. Meanwhile, an energy-efficient coding-aware metric, named EECAM, is presented which detects coding opportunities within two-hop neighbors and selects nodes with high remaining energy. Finally, the EECAR mechanism is implemented in ns-2. Simulation results demonstrate that the proposed EECAR strategy achieves significant throughput gains and prolongs the network lifetime.

Amis, A.D [65] presented an ad hoc network may be logically represented as a set of clusters. The clusterheads form a d-hop dominating set. Each node is at most d hops from a clusterhead. Clusterheads form a virtual backbone and may be used to route packets for nodes in their cluster. Previous heuristics restricted themselves to 1-hop clusters. The researchers show that the minimum d-hop dominating set problem is NP-complete. Then the researchers present a heuristic to form d-clusters in a wireless ad hoc network. Nodes are assumed to have a non-deterministic mobility pattern. Clusters are formed by diffusing node identities along the wireless links. When the heuristic terminates, a node either becomes a clusterhead, or is at most d wireless hops away from its clusterhead. The value of d is a parameter of the heuristic. The heuristic can be run either at regular intervals, or whenever the network configuration changes. One of the features of the heuristic is that it tends to re-elect existing clusterheads even when the network configuration changes. This helps to reduce the communication overheads during transition from old clusterheads to new clusterheads. Also, there is a tendency to evenly distribute the mobile nodes among the clusterheads, and evently distribute the responsibility of acting as clusterheads among all nodes. Thus, the heuristic is fair and stable. Simulation experiments demonstrate that the proposed heuristic is better than the two earlier heuristics, namely the LCA and degree-based solutions.

T.Camp, [66] proposed in the performance evaluation of a protocol for an ad hoc network, the protocol should be tested under realistic conditions including, but not limited to, a sensible transmission range, limited buffer space for the storage of messages, representative data traffic models, and realistic movements of the mobile users (i.e., a mobility model). This paper is a survey of mobility models that are used in the simulations of ad hoc networks. The researchers describe several mobility models that represent mobile nodes whose movements are independent of each other (i.e., entity mobility models) and several mobility models that represent mobile nodes whose movements are dependent on each other (i.e., group mobility models). The goal of this paper is to present a number of mobility models in order to offer researchers more informed choices when they are deciding upon a mobility model to use in their performance evaluations. Lastly, the researchers present simulation results that illustrate the importance of choosing a mobility model in the simulation of an ad hoc network protocol. Specifically, the researchers illustrate how the performance results of an ad hoc network protocol drastically change as a result of changing the mobility model simulated.

A.Amis & R.Prakash [67] introduces Ad hoc networks consisted of a set of identical nodes that move freely and independently and communicate with other node via wireless links. Such networks may be logically represented as a set of clusters by grouping together nodes that are in close proximity with one another. Clusterheads form a virtual backbone and may be used to route packets for nodes in their cluster. Nodes are assumed to have non-deterministic mobility pattern. Clusters are formed by diffusing node identities along the wireless links. Different heuristics employ different policies to elect cluster-heads. Several of these policies are biased in favor of some nodes. As a result, these nodes shoulder greater responsibility and may deplete their energy faster, causing them to drop out of the network. Therefore, there is a need for load-balancing among clusterheads to allow all nodes the opportunity to serve as a clusterhead. The researchers propose a load balancing heuristic to extend the life of a clusterhead to the maximum budget before allowing the clusterhead to retire and give way to another node. This helps to evenly distribute the responsibility of acting as clusterheads among all nodes. Thus, the heuristic insures fairness and stability. Simulation experiments demonstrate that the proposed heuristic does provide longer clusterhead durations than with no load-balancing.

S.Banerjee & S.Khuller [68] presented a clustering scheme to create a hierarchical control structure for multi-hop wireless networks. A cluster is defined as a subset of vertices, whose induced graph is connected. In addition, a cluster is required to obey certain constraints that are useful for management and scalability of the hierarchy. All these constraints cannot be met simultaneously for general graphs, but the researchers shown how such a clustering can be obtained for wireless network topologies. Finally, the researchers presented an efficient distributed implementation of their clustering algorithm for a set of wireless nodes to create the set of desired clusters.

A.B.McDonald & T.Znati [69] presented a novel framework for dynamically organizing mobile nodes in wireless ad hoc networks into clusters in which the probability of path availability can be bounded. The purpose of the (α t) cluster is to help minimize the far-reaching effects of topological changes while balancing the need to support more optimal routing. A mobility model for ad hoc networks is developed and is used to derive expressions for the probability of path availability as a function of time. It is shown how this model provides the basis for dynamically grouping nodes into clusters using an efficient distributed clustering algorithm. Since the criteria for cluster organization depends directly upon path availability, the structure of the cluster topology is adaptive with respect to node mobility. Consequently, this framework supports an adaptive hybrid routing architecture that can be more responsive and effective when mobility rates are low and more efficient when mobility rates are high

S.Muthuramalingam and R.Rajaram [70] presented a novel algorithm for clustering of nodes by transmission range based clustering (TRBC).This algorithm does topology management by the usage of coverage area of each node and power management based on mean transmission power within the context of wireless ad-hoc networks. By reducing the transmission range of the nodes, energy consumed by each node is decreased and topology is formed. A new algorithm is formulated that helps in reducing the system power consumption and prolonging the battery life of mobile nodes. Formation of cluster and selection of optimal cluster head and thus forming the optimal cluster taking weighted metrics like battery life, distance, position and mobility is done based on the factors such as node density, coverage area, contention index, required and current node degree of the nodes in the clusters.

Preetee K. Karmore & Smita M. Nirkhi [71] proposed a MANET has no clear line of defense so; it is accessible to both legitimate network nodes and malicious nodes. Some of the nodes may be selfish, for example, by not forwarding the packets to the destination, thereby saving the battery power. Some others may act malicious by launching security attacks like black hole or hack the information. Traditional way of protecting networks with firewalls and encryption software is no longer sufficient. Therefore, intrusion detection system is required that monitor the network, detect malicious node and notifies other node in the network to avoid malicious node i. e. IDS detects malicious activities in the networks. The researchers have implemented k-means clustering algorithm of data mining for efficient detection of intrusions in the MANET traffic and also generated black hole attacks in the network. In data mining, clustering is the most important unsupervised learning process used to find the structures or patterns in a collection of unlabeled data. The researchers have used the K-means algorithm to cluster and analyze the data in this paper. The simulation of the proposed method is performed in NS2 simulator and the researchers got the result as the researchers expected.

Cynthia Jayapal and Sumathi Vembu [72] presented an adaptive service discovery protocol that enhances the performance of service discovery. The existing service discovery procedures, uses either centralized, distributed or hybrid architectures. These architectures use different methods of service registration, advertisement, selection, discovery modes and state maintenance to improve the service discovery performance, but they use the conventional methods for selecting a core node that aids in all the service discovery phases. Their main focus is to use an adaptive core node election mechanism that changes whenever the load increases and is also robust against network failures. This enhances the performance of discovery due to the reduction in frequent handoffs. The researchers used a distributed directory based service discovery mechanism that operates in a proactive mode with service advertisements to the core node and selects a provider based both on distance and service capability of the provider. Their simulation results show that their adaptive service discovery scheme performs better in terms of service discovery success ratio, control message over head, discovery delay and the number of hand offs, when compared to conventional schemes.

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