IEEHR: Improved Energy Efficient Honeycomb based Routing in MANET for Improving Network Performance and Longevity

In present scenario, efficient energy conservation has been the greatest focus in Mobile Adhoc Networks (MANETs). Typically, the energy consumption rate of dense networks is to be reduced by proper topological management. Honeycomb based model is an efficient parallel computing technique, which can manage the topological structures in a promising manner.  Moreover, discovering optimal routes in MANET is the most significant task, to be considered with energy efficiency. With that motive, this paper presents a model called Improved Energy Efficient Honeycomb based Routing (IEEHR) in MANET. The model combines the Honeycomb based area coverage with Location-Aided Routing (LAR), thereby reducing the broadcasting range during the process of path finding. In addition to optimal routing, energy has to be effectively utilized in MANET, since the mobile nodes have energy constraints. When the energy is effectively consumed in a network, the network performance and the network longevity will be increased in respective manner. Here, more amount of energy is preserved during the sleeping state of the mobile nodes, which are further consumed during the process of optimal routing. The designed model has been implemented and analyzed with NS-2 Network Simulator based on the performance factors such as Energy Efficiency, Transmission Delay, Packet Delivery Ratio and Network Lifetime

The Honeycomb Architecture: Prototype Analysis and Design

Due to the inherent potential of parallel processing, a lot of attention has focused on massively parallel computer architecture. To a large extent, the performance of a massively parallel architecture is a function of the flexibility of its communication network. The ability to configure the topology of the machine determines the ease with which problems are mapped onto the architecture. If the machine is sufficiently flexible, the architecture can be configured to match the natural structure of a wide range of problems. There are essentially four unique types of massively parallel architectures: 1. Cellular Arrays 2. Lattice Architectures [21, 30] 3. Connection Architectures [19] 4. Honeycomb Architectures [24] All four architectures are classified as SIMD. Each, however, offers a slightly different solution to the mapping problem. The first three approaches are characterized by easily distinguishable processor, communication, and memory components. In contrast, the Honeycomb architecture contains multipurpose processing/communication/memory cells. Each cell can function as either a simple CPU, a memory cell, or an element of a communication bus. The conventional approach to massive parallelism is the cellular array. It typically consists of an array of processing elements arranged in a mesh pattern with hard wired connections between neighboring processors. Due to their fixed topology, cellular arrays impose severe limitations upon interprocessor communication. The lattice architecture is a somewhat more flexible approach to massive parallelism. It consists of a lattice of processing elements embedded in an array of simple switching elements. The switching elements form a programmable interconnection network. A lattice architecture can be configured in a number of different topologies, but it is still only a partial solution to the mapping problem. The connection architecture offers a comprehensive solution to the mapping problem. It consists of a cellular array integrated into a packet-switched communication network. The network provides transparent communication between all processing elements. Note that the communication network is physically abstracted from the processor array, allowing the processors to evolve independently of the network. The Honeycomb architecture offers a unique solution to the mapping problem. It consists of an array of identical processing/communication/memory cells. Each cell can function as either a processor cell, a communication cell, or a memory cell. Collections of Honeycomb cells can be grouped into multicell CPUs, multi-cell memories, or multi-cell CPU-memory systems. Multi-cell CPU-memory systems are hereafter referred to as processing clusters. The topology of the Honeycomb is determined at compilation time. During a preprocessing phase, the Honeycomb is adjusted to the desired topology. The Honeycomb cell is extremely simple, capable of only simple arithmetic and logic operations. The simplicity of the Honeycomb cell is the key to the Honeycomb concept. As indicated in [24], there are two main research avenues to pursue in furthering the Honeycomb concept: 1. Analyzing the design of a uniform Honeycomb cell 2. Mapping algorithms onto the Honeycomb architecture This technical report concentrates on the first issue. While alluded to throughout the report, the second issue is not addressed in any detail

Honeycomb : indoor location estimation based on Wi-Fi signal strength

textThis paper presents Honeycomb, an indoor location estimation product based on Wi-Fi signal strength. Wireless Local Area Networks are ubiquitous today, and most people carry Wi-Fi capable devices in their pocket. This existing infrastructure can thus be leveraged for purposes of location estimation. Using Wi-Fi signal strength fingerprinting, Honeycomb harnesses existing Wi-Fi infrastructures as a means to track the movements of individuals through an indoor space. Fingerprinting is a method by which Wi-Fi signal strengths are mapped at regular intervals in a bounded space. Once a space is fingerprinted, a given node must simply sample Wi-Fi signal strengths as it moves through the same space and Honeycomb's algorithm will determine the node’s path in an offline manner. Because Honeycomb only requires nodes to passively measure Wi-Fi signal strengths rather than send out its own beacon, it prevents malicious third parties from gaining access to any real time data, and thus maintains the security and privacy of the user. By performing location estimations on the data collected on an independent platform, and not on the device itself, it saves the user from spending the computing power, and thus the device's battery. We believe Honeycomb to be a product unlike any other, which is suitable for deployment in multiple real world scenarios.Electrical and Computer Engineerin

Interconnection Networks Based on Gaussian and Eisenstein-Jacobi Integers

Quotient rings of Gaussian and Eisenstein-Jacobi(EJ) integers can be deployed to construct interconnection networks with good topological properties. In this thesis, we propose deadlock-free deterministic and partially adaptive routing algo­rithms for hexagonal networks, one special class of EJ networks. Then we discuss higher dimensional Gaussian networks as an alternative to classical multidimen­sional toroidal networks. For this topology, we explore many properties including distance distribution and the decomposition of higher dimensional Gaussian net­ works into Hamiltonian cycles. In addition, we propose some efficient communi­cation algorithms for higher dimensional Gaussian networks including one-to-all broadcasting and shortest path routing. Simulation results show that the rout­ing algorithm proposed for higher dimensional Gaussian networks outperforms the routing algorithm of the corresponding torus networks with approximately the same number of nodes. These simulation results are expected since higher dimen­sional Gaussian networks have a smaller diameter and a smaller average message latency as compared with toroidal networks. Finally, we introduce a degree-three interconnection network obtained from pruning a Gaussian network. This network shows possible performance improve­ment over other degree-three networks since it has a smaller diameter compared to other degree-three networks. Many topological properties of degree-three pruned Gaussian network are explored. In addition, an optimal shortest path routing algorithm and a one-to-all broadcasting algorithm are given

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.Siirretty Doriast