An Energy Efficient Network Architecture for Infrastructured Wireless Networks 2010 IEEE Global Telecommunications Conference (GLOBECOM 2010) Presented by: Qiang YE

Introduction Wireless Networks Infrastructure: Ad Hoc Network: Nodes act as both router and host; Multi-hop communications 2. Cellular Network or WLAN: Communications take place through BSs(APs); BSs provide services to the mobile users (MU) 4/10/2018

Example: The Airport Scenario The architecture of the entire airport can be divided into an array of equal size cells and each cell contains a basestation which provides services to all mobile users in the cell (Fig.1). 4/10/2018

From the deployment and user association point of view, this kind of infrastructure may not be communication efficient, since users should be always associated with the nearest BS to save the transmit power of the BS on a large scale. In most power attenuation models, the signal power falls as 1/dα , where d is the distance from the transmitter and α is the pathloss coefficient. Thus, the communication is always energy efficient if the distance between the BS and the MU is smaller. 4/10/2018

However, in a practical network such as a network in an airport building, it may not be possible to place basestations on a regular grid with a basestation at the center of each cell, like the network structure in Fig.1, due to the reasons of obstacles, building shapes, geographical areas, and barriers under the airport architecture. In this paper , the author proposes a closest neighbor approach using Voronoi diagrams to reduce the energy consumption during data transmission between MUs and BSs. 4/10/2018

The proposed architecture uses a new distributed algorithm, called localized Voronoi diagram, to divide the cell structure in a another way. The main benefit of this algorithm is that it can guarantee the MU always communicates with the BS with the shortest distance among all the BSs in the network This algorithm gives us another way of thinking in BSs deployment and user association when dealing with energy conservation issue in Green Networks 4/10/2018

Fig.2. Eliminate nodes ai+2 and ai+3 4/10/2018

In the algorithm, each node u collects 1-hop neighborhood information n1(u) and draws a perpendicular bisector for each neighbor. The Voronoi region is formed by connecting all the bisector line segments from the intersection points, and the resulting graph is called Voronoi diagram, see the Fig. 3. Fig.3. The Voronoi regions 4/10/2018

The run time complexity of this algorithm is O(Δ2), where Δ is the maximum node degree. Please refer to the paper for the detailed proof of its running complexity. Theorem 1: For each node u in Voronoi diagram, every nearest neighbor of u defines an edge in the Voronoi region.(see Fig.3. ) 4/10/2018

The network architecture using Voronoi diagram We construct the Voronoi diagram using Algorithm 1 only with the set of basestations, see Fig. 3. Each basestation provides service only to the mobile users existing in the Voronoi region formed by the basestation. The kind of network infrastructure has the following advantages: 1. Energy efficient communications: always connect with (or handover to) the BS having the shortest distances from it. 4/10/2018

2. Random distribution: In this proposal, first the BSs are placed randomly followed by the construction of cells based on the placement. 3. Scalable: Whenever a new BS is added to the existing system, it is easy to append the Voronoi region to the structure. Similarly, it is easy to update the Voronoi diagram whenever a BS is down or is deleted from the existing system. The updating of cell structure is done using local information. 4. Localized algorithm: It is always preferable to use localized algorithms because they maintain only local information and do not require global information which is costly to manage. 5.Minimum Latency: because of the shortest communication distance, smaller delays can be guaranteed. 4/10/2018

Mobility Management Issue For efficient communication, there is a need for a basestation to maintain the updated list of MUs when the MUs move out or join the Voronoi region. Also, each MU has to keep track of the point of attachment when it moves around the network. Two methods to maintain the mobility management issue: periodic broadcasting technique: each BS broadcasts its location and Voronoi region information at every time interval tint1 using the message packet voronoi.(not suitable for realtime services) 4/10/2018

2. Prediction Heuristics: In this method, each MU predicts time tp at which the MU crosses the Voronoi region and sends this information to the basestation. The tp is computed as follows: The point (p2, q2) is the intersection point of the line equation and a Voronoi edge. 4/10/2018

This method is more suitable to the real time applications A current point of attachment of a MU removes the mobile user from MU list and starts handing over control to the neighboring basestation if the value of tp is less than or equal to tthresh. The tthresh value depends on the application and degree of fairness required during handoff. If the value of tp is greater than the value of tthresh then it waits for twait time and repeats the process of checking for tp ≤ tthresh. This method is more suitable to the real time applications 4/10/2018

Simulations and Performance The simulation platform is the NS2.28 The simulation is the comparison of energy consumption in between MUs and BSs packet transmissions under both equalized cell architecture and the voronoi diagram based architecture. We have 50 nodes in the network, 9 nodes are considered as BSs (one for each cell) and remaining 41 are mobile users are deployed randomly. 4/10/2018

The transmission range of each node is 100m The transmission range of each node is 100m. A constant bit rate (CBR) source is used to generate the packets. The packet size is 512 bytes. The total simulation time is 600 seconds. The initial energy given for each node is 5000J. The energy consumptions for transmission, reception, and idle power are 0.002J, 0.0002J, and 0.000001J respectively. In the simulation ,we test the energy consumption from MUs to BSs for all the packet transmission, receptions, and idle states within the simulation time. 4/10/2018

We have plotted the graphs for average, total, minimum, and maximum energy consumptions in Fig 4, Fig. 5, Fig. 6, and Fig. 7, respectively 4/10/2018

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conclusion In this paper, we have proposed a localized algorithm for constructing the Voronoi diagram in wireless networks and used this algorithm to reduce the energy consumption in the defined network model. This proposal not only reduces the per packet energy consumption, but also increases the network lifetime. The proposed architecture has other advantages such as smaller latency and randomization. 4/10/2018

Thank you ! 4/10/2018