VIOLENCIA SIMBÓLICA EN LA ACCIÓN DE MEDIOS

Also called cluster-based protocols, is a two tires protocol known for their scalability and efficient communication. Originally was developed for traditional wired-networks but latter was utilized to perform energy-efficient routing in sensor networks. A single tire network can cause congestion at the gateway especially with high sensors density. This leads to communication delays and inadequate tracking of events in addition to limited scalability. To overcome these problems without degrading the service, network clustering has been proposed in some routing approaches. In hierarchical architectures clusters are created and special tasks are assigned to cluster-heads, this must be done with intensive care because it has a great impact on the overall system scalability, network lifetime, and energy consumption. In hierarchical protocols high-energy nodes can be used to process and send information while low-energy nodes can perform sensing. Hierarchical routing is two-tire routing where one tire is used to elect cluster-heads and the other

for routing. Cluster-heads reduce energy consumption by performing data aggregation and fusion which decrease messaging towards the sink.

In this section we will review hierarchical routing protocols.

3.3.1 LEACH

Low Energy Adaptive Clustering Hierarchy (LEACH) [10] is one of the first hierarchal routing algorithms for sensor networks. LEACH randomly select sensor nodes as cluster-heads and rotates this role to distribute energy load among the sensors since using the same node will deplete its energy. The cluster heads aggregates data received from nodes to reduce the number of messages sent to the sink. This approach is well-suited for applications where constant monitoring is needed in which data periodic collection is centralized. This will save energy as only cluster-head nodes transmits to the sink rather that all sensor nodes. Based on simulations the optimal number of cluster heads is estimated to be five percent of the total number of nodes.

The operation of LEACH is split into two phases, the setup phase and the steady state phase. In order to minimize overhead the duration of steady phase is longer than the setup phase. During the setup phase, the clusters are created and cluster heads are selected. This selection is made by the node choosing a random number between zero and one. The sensor node is a cluster-head if this random number is less than the threshold T(n) calculated as the following:

Where P is the desired percentage to become a cluster head, r is the current round, and G is the set of nodes that have been selected as a cluster head in the last 1/P rounds. After cluster-heads are chosen they broadcast an advertisement to the entire network that they are the new

T(n)=

P/[1-P*(rmod(1/P))] if n Є G 0 otherwise

cluster-heads. Every node receiving the advertisement decides to which cluster they want to belong depending on the signal strength. The sensor node sends a message to register with cluster-head of their choice. The cluster-head based on a TDMA approach assigns each node registered in its cluster a time slot when it can send data.

During the sensing phase cluster nodes can start sensing and transmitting data to the cluster-heads. All the data processing such as data fusion and aggregation are local to the cluster. After a certain period of time spent on the steady phase, the network enters the setup phase again and start anew round of selecting cluster-heads.

LEACH is able to increase the network lifetime. It achieves over a factor of seven reduction in energy dissipation compared to direct communication and a factor of four to eight compared to the minimum transmission energy routing protocol. LEACH has a number of drawbacks listed in [5]:

1. Assumes that all nodes can transmit with enough power to reach the sink.

2. It is not applicable to networks deployed in large regions.

3. Dynamic clustering brings extra overhead, e.g. head changes, advertisements, etc., which may diminish the gain in energy consumption.

4. Assumes that all nodes begin with the same amount of energy and a cluster-head consumes approximately the same amount of energy.

5. Assigns time slot to each node even it node has no data to transmit.

SPIN LEACH Directed-diffusion

Optimal route No No Yes

Network lifetime Good Very good Good Resource awareness Yes Yes Yes Use of meta-data Yes No Yes

Table 1: Comparison between SPIN, LEACH, and Directed-diffusion

Leach with negotiation is an extension of LEACH proposed in [10]. It use meta-data as in SPIN prior data transfer to ensure that only interesting data is transmitted to head-clusters before being transmitted to the sink.

Table 2.1 taken from [5] shows a small comparison between SPIN, LEACH, and directed-diffusion.

3.3.2 PEGASIS

Power-Efficient GAthering in Sensor Information Systems an enhancement over the LEACH protocol was proposed in [31]. As opposed to LEACH, PEGASIS has no clusters, instead it creates chains from sensor nodes so that each node communicate only with their closest neighbours and only one node is selected from the chain to communicate with the sink. When the round of all nodes communicating with the sink ends, a new round starts and so on. This allows distributing energy consumption uniformly among all nodes. PEGASIS forms near optimal chins in a greedy way. The use of collaborative techniques increases node and network lifetime in addition it reduce the communication bandwidth by local coordination between close nodes.

PEGASIS nodes use signal strength to measure the distance to neighbouring nodes. Each node aggregate data to be sent to the sink by any node in the chain and the nodes in the chain will take turns sending to the sink. Simulation results in [31] showed that PEGASIS outperformed LEACH about 100 to 300% for different network sizes and topologies.

This performance is achieved through the use of data aggregation and the reduction of overhead brought by cluster formation. In order to rout its data, nodes need to know about the energy level of its neighbours which requires dynamic adjustments of PEGASIS topology. Such topological adjustments, especially in high utilized networks, may introduce significant overhead. Another important drawback is the absence of

methods by which nodes determine its location in a network; it is assumed that all nodes maintain a complete database of the location of all other nodes in the network. Moreover, this protocol assumes that each sensor node can communicate with the sink directly and does not outline multi-hop communication to reach the sink. In addition, PEGASIS assumes that all nodes start with the same level of energy and consumption rates are equal. Also the single head of the chain can become a bottleneck and distant nodes may suffer from excessive delays.

Finally, in terms of modelling, this approach also assumes that nodes are stationary.

An extension to PEGASIS is Hierarchical-PEGASIS proposed in [32] to decrease delay incurred for packets during transmission to the sink and suggests energy X delay metric to solve data gathering problem.

Simultaneous data messages are utilized to reduce delay. Two approaches were studied to avoid possible collisions and signal interference: signal coding and spatial transmission. In the latter one only spatially separated modes are allowed to transmit at the same time. This chain-based protocol with CDMA capable nodes, constructs a chin of nodes that forms a tree like hierarchy and each selected node in a particular level transmits data to the node in the upper level of the hierarchy. This method ensures data transmitting in parallel and reduces the delay significantly. Since nodes are not aware of their neighbour's energy levels, Hierarchical-PEGASIS still require dynamic topological adjustment. Even though, they have performed better than standard PEGASIS by a factor of about 60.

3.3.3 TEEN

Threshold-Sensitive Energy Efficient Protocols [33] were proposed for time-critical application in which network operate in a reactive mode.

TEEN utilizes a hierarchical approach along with data-centric mechanism. It is very suitable for situations where the environment is sensed continuously but data transmission is done less frequently. The basic idea behind this approach is the grouping of closer nodes into clusters and this process goes on the second level until the sink is reached. Figure 6 redrawn from [33] shows TEEN networks architecture.

Figure 6: Hierarchical Clustering in TEEN & APTEEN

Cluster-heads broadcasts to its members a head threshold (which is the minimum possible value of the sensed attribute to be sent to cluster-head) and a soft threshold (which is a small change in the value of the sensed attribute that triggers the node to switch on its transmitter and transmit).

Thus, the hard threshold tries to reduce the number of transmissions by enforcing nodes to transmit only when sensed attribute is in the range of interest. While the soft threshold reduces the numbers of transmissions by blocking all messages about little or no change in the sensed attribute.

As a result, the user can control the trade off between energy efficiency and data accuracy; smaller value of the soft threshold gives more accurate

picture of the environment but consumes more energy on the other hand.

Note that the user can change both threshold values as required but this is impractical for applications where periodic reports are needed, since the broadcast message may be lost consequently nodes will never communicate.

The Adaptive Threshold sensitive Energy Efficient sensor Network protocol (APTEEN) [34] is an enhancement version of TEEN that changes the periodicity or threshold value used in the TEEN protocol.

The architecture is the same as TEEN but here the cluster-head broadcast four parameters:

 Attributes: set of physical parameters that user is interested with

 Thresholds: hard threshold and soft threshold

 Schedule: a TDMA schedule, assigning time slot to each node to transmit

 Count time: maximum time between two successive reports sent by a node

Once a node sense a change in some attribute value equal to or greater than the soft threshold or sense a value greater than the hard threshold it transmits data to cluster-head. Despite the improvements over TEEN, APTEEN has added additional complexity to implement new parameters (Attribute, Count Time, and Schedule). On the other hand, it offers more flexibility and combines proactive and reactive techniques. Simulation results have shown that both TEEN and APTEEN has outperformed LEACH [10]. In terms of energy dissipation and network lifetime, TEEN gives the best results while APTEEN is between LEACH and TEEN.

3.3.4 Self-organizing protocol

Subramanian et al. [35] describes not only a self-organizing protocol, but also application taxonomy. The proposed taxonomy was used to build

architecture and infrastructure components to support heterogeneous sensors. Some sensors, could be mobile or stationary, probe the environment and transmit messages to a designated set of stationary nodes that act as routers. Each node should be reachable by a router that forms the backbone for transmitting data to more powerful sink nodes.

Also each sensing node was assumed to have unique identifier and they can be identified by the address of the router node to which they are connected. The routing architecture is hierarchical where groups of nodes are formed and merge when needed. Fault tolerance is achieved by using Local Markov Loops (LML) algorithm in broadcast trees. In this approach router nodes keep the entire sensor connected by forming a dominating set. The phase of self-organizing router nodes and initializing routing tables are:

 Discovery phase: discover neighbouring nodes

 Organization phase: groups are formed and merged where each node is identified by address or router node it is connected to.

Routing tables and broadcast tree and built

 Maintenance phase: routing tables update messages are exchanged and broadcast trees are maintained by LML

 Self-reorganization phase: when node or partition failure occurs Since the sensor nodes can be addressed individually in the routing architecture, this approach is suitable for applications where communication to a particular node is required. Moreover, the cost for maintaining routing tables and keeping a balanced routing hierarchy is minimized which is one of the several strength points in this approach.

As for broadcasting a message is less than that consumed in SPIN protocol [22]. However, this is not on-demand protocol which adds additional overhead in the organization phase of the algorithm.

Furthermore, in case of many cuts in the network the hierarchy forming

will be every expensive because networks cuts increase the probability of applying reorganization phase.