Modelling and Simulation of Game Applications in Ad Hoc Wireless Networks Routing

2012 ◽  
pp. 1-21
Author(s):  
Omar Raoof ◽  
Hamed Al-Raweshidy
Keyword(s):  
Power Consumption ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Equilibrium Path ◽  
Random Allocation ◽  
Intermediate Node ◽  
Auction Mechanism ◽  
Sealed Bid

This chapter presents a game theory based routing algorithm that defines the best route based on the power consumption that each intermediate node will suffer to forward a packet, the price the destination will pay to the source, and the amount of compensation the source will pay to each intermediate node. The chapter also presents a polynomial time algorithm that can give a Nash Equilibrium path and use it to evaluate the performance of the game. The key features of the introduced mechanism are: it uses the first and second price auctions; the auction mechanism insures a fare allocation of the data to the user who values it the most; the second-price sealed-bid auction gives better revenue to the source when compared to the random allocation scheme and the first-price sealed-bid mechanism; the game mechanism combines both source compensation to the intermediate nodes and the power consumption to improve the path reliability between the source and the destination (i.e. the winning bidder); the source payoff will increase once the network density increases; and, finally, the simulation results prove that the introduced auction mechanism dramatically increases the destination’s revenue whether the first or second-price auction is chosen.

scholarly journals DISTANCE AND HOPS-BASED ENERGY ESTIMATION IN WIRELESS SENSOR NETWORKS

2017 ◽  
Vol 10 (13) ◽  
pp. 85 ◽  
Author(s):  
Pradeep Kumar Ts ◽  
Sayali Chitnis
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Transmission Power ◽  
Energy Estimation ◽  
Wide Range ◽  
Implementation Level

The world of internet of things (IoT) and automation has been catching a robust pace to impact wide range of commercial and domestic applications for some time now. The IoT holds ad-hoc or wireless sensor networks (WSNs) at its very primary implementation level, the hardware level. The increasing requirement of these networks demands a renewed and better design of the network that improves the already existing setbacks of WSNs, which is mainly the power consumption and optimization. Routing highly affects the power consumed in the nodes in WSNs, hence having a modified routing algorithm which is specific to the application and meets its needs, particularly it is a good approach instead of having a generalized existent routing approach. Currently, for WSN having adequate number of nodes, routing involves maximum number of nodes and hops so as to reduce power consumption. However, for restricted areas and limited nodes, this scenario concludes with using up more number of nodes simultaneously resulting in reducing several batteries simultaneously. A routing algorithm is proposed in this paper for such applications that have a bounded region with limited resources. The work proposed in this paper is motivated from the routing algorithm positional attribute based next-hop determination approach (PANDA-TP) which proposes the increase in number of hops to reduce the requirement of transmission power. The aim of the proposed work is to compute the distance between the sending and receiving node and to measure the transmission power that would be required for a direct(path with minimum possible hops) and a multi-hop path. If the node is within the thresh-hold distance of the source, the packet is undoubtedly transferred directly; if the node is out of the thresh-hold distance, then the extra distance is calculated. Based on this, the power boosting factor for the source node, and if necessary, then the extra number of nodes that would be required to transmit is determined. An extra decision-making step is added to this approach which makes it convenient to utilize in varied situations. This routing approach suits the current level of robustness that the WSNs demand. 

Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

Cluster-Based Location Aided Routing Algorithm for Mobile Ad Hoc Networks

2009 ◽  
Vol 20 (11) ◽  
pp. 3086-3100
Author(s):  
Yi WANG ◽  
Liang DONG ◽  
Tao-Tao LIANG ◽  
Xin-Yu YANG ◽  
De-Yun ZHANG
Keyword(s):  
Ad Hoc Networks ◽  
Mobile Ad Hoc Networks ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Mobile Ad Hoc ◽  
Hoc Networks

scholarly journals Metric Dimension Parameterized By Treewidth

Algorithmica ◽  
2021 ◽  
Author(s):  
Édouard Bonnet ◽  
Nidhi Purohit
Keyword(s):  
Computable Function ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Minimum Size ◽  
Metric Dimension ◽  
Resolving Set ◽  
Input Graph ◽  
Outerplanar Graphs ◽  
Bounded Treewidth ◽  
Fpt Algorithm

AbstractA resolving set S of a graph G is a subset of its vertices such that no two vertices of G have the same distance vector to S. The Metric Dimension problem asks for a resolving set of minimum size, and in its decision form, a resolving set of size at most some specified integer. This problem is NP-complete, and remains so in very restricted classes of graphs. It is also W[2]-complete with respect to the size of the solution. Metric Dimension has proven elusive on graphs of bounded treewidth. On the algorithmic side, a polynomial time algorithm is known for trees, and even for outerplanar graphs, but the general case of treewidth at most two is open. On the complexity side, no parameterized hardness is known. This has led several papers on the topic to ask for the parameterized complexity of Metric Dimension with respect to treewidth. We provide a first answer to the question. We show that Metric Dimension parameterized by the treewidth of the input graph is W[1]-hard. More refinedly we prove that, unless the Exponential Time Hypothesis fails, there is no algorithm solving Metric Dimension in time $$f(\text {pw})n^{o(\text {pw})}$$ f ( pw ) n o ( pw ) on n-vertex graphs of constant degree, with $$\text {pw}$$ pw the pathwidth of the input graph, and f any computable function. This is in stark contrast with an FPT algorithm of Belmonte et al. (SIAM J Discrete Math 31(2):1217–1243, 2017) with respect to the combined parameter $$\text {tl}+\Delta$$ tl + Δ , where $$\text {tl}$$ tl is the tree-length and $$\Delta$$ Δ the maximum-degree of the input graph.

Research on MANET Routing Algorithm Based on Ant Colony Algorithm

2011 ◽  
Vol 135-136 ◽  
pp. 781-787
Author(s):  
Yong Feng Ju ◽  
Hui Chen
Keyword(s):  
Network Lifetime ◽  
Ant Colony Algorithm ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Packet Transmission ◽  
Ant Colony ◽  
Dynamic Allocation ◽  
Routing Table ◽  
Manet Routing ◽  
Rate Dynamic

This paper proposed a new Ad Hoc dynamic routing algorithm, which based on ant-colony algorithm in order to reasonably extend the dynamic allocation of network traffic and network lifetime. The Algorithm choose path according transmission latency, path of the energy rate, congestion rate, dynamic rate. The Algorithm update the routing table by dynamic collection of path information after path established. The analyse shows that algorithm increases the network throughput, reduces the average end-to-end packet transmission latency, and extends the network lifetime, achieves an improving performance.

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