Cooperation Communication Strategies for Extending Wireless Sensor Network Lifetime

Cooperation Communication Strategies for Extending Wireless Sensor Network Lifetime
Lama Alhusaini
Abstract—Wireless Sensor Network (WSN) is a system composed of numerous sensor bulges which are communicated by transmitting and relaying packets. These nodes are capable of sensing information, dispensing data and communicating wirelessly. Conversely, these nodes have limited battery capacities. The communication between these nodes is one of the major factors that cause the energy indulgence of the nodes. To overcome this problem, many routing methods have been suggested in important study work for refining the energy efficacy. Among these routing methods is cooperative communication between sensor nodes. The results of the researches showed that the cooperative packet forwarding strategies can minimize sensor nodes energy consumption and maximize the data collection rate, and thus extend network lifetime of WSN. This paper discusses the cooperation communication strategies and the parameters that affect cooperation on WSN lifetimes such as quantity of domains, network area, network topology, and base station (BS) deployment.
Introduction
WSNs have many diverse applications ranging from data monitoring to real-time industrial applications as in [Vaz de Melo et al. 2013]. WSNs can be deployed in the harsh environment such as forests, volcanoes, and deserts to monitor physical phenomena. Recently, as in [Kinoshita et al. 2016] it becomes more common that multiple overlapping WSNs, owned by different authorities, have been constructed within the same physical location and their sensors nodes cooperate with other networks.

In such situation, the lifetime of each network is expected to be extended by cooperative packet forwarding as in [Inoue et al. 2016].One of the important challenges in the design of a WSN is prolonging network lifetime by decreasing the nodes’ energy usage since they have a limited power supply which is difficult to substitute or renew as in [Vaz de Melo et al. 2013]. The network lifetime as in [Yildiz et al. 2016] is defined as the period from the moment the network begins to operate to the moment when the main sensor node drains out the whole of its energy resource (i.e., battery).
As in [Inoue et al. 2016] WSN composed of sensor nodes with multiple functions and which use low power deployed in a huge area with a single or a few sinks that collect data from sensor nodes. As in [Bicakci and Tavli 2010] base stations (BSs) operate as data sinks that can be supplied with power easily. Each node transmits its packets to a sink either directly or by communications with multiple sensor nodes. Data communication uses a noteworthy quantity of energy. Energy consumption of nodes around a sink is significantly higher than other nodes because they have to transmit and relay more packets; therefore, they tend to die earlier than other nodes. Thus, to assure an efficient operability of the WSN, sensor nodes should cooperate among themselves to increase the eminence of the data and optimize the energy consumption in the communication process as in [Vaz de Melo et al. 2013].
Constructing the system model and solving the optimization
As in [Shamani et al., 2013], the goal of the paper is stated clear in this section, which is to estimate the highest lifetime of a sensor network that can be achieved by using multiple domains. First, the system model is expressed with the objective of optimizing and setting problem limits.
System model
In this section, it is communication and not detection and dispensation of energy that dominates the energy consumption of sensor nodes. For this reason, the sensor nodes are preloaded with a battery that carries an equal amount of finite energy. As in [Li and Mohapatra 2007], this technique includes a space-time coding system which has layered space-time designs. Evidently, it capable of enhancing channel capacity as well as reducing transmission energy depletion specifically in channels that are fading. Also, they can provide high network enactment and do not require extra bandwidth and power for transmission. The figure below illustrates how a two-domain sensor network works. There are different tangibly different areas that have separate sinks. Also the distribution of the sensor nodes is different for the purpose of monitoring a circular area and reporting back to the sinks. The only time that intra-communication can happen is if there lacks cooperation between the domains.

Solving optimization problem
The very first step when solving an engineering problem is building a model that captures the indispensable features of a physical phenomenon that exists. In a mathematical software design model, there are two rudimentary elements. The first one is the function that should be augmented while the second one the constraints set. Therefore, when the functions, as well as the constraints that are being optimized above, are examined, it is evident that they are linear. Therefore, it is important to consider a simple topology that consists of one base station and two cooperating sensor nodes.
One-dimensional linear sensor network
This section involves the investigation of the lifetime of a sensor network with cooperation in rectilinear topology networks. Note that there are numerous of wireless sensor network organization scenarios envisioned for these types of topologies. However, the main motivation, in this case, is to explore the effects of cooperation and non-cooperation in a setting that is simple and with line networks that are well suited for this purpose. As in [Shamani et al., 2013], the first thing is to solve the problem for a network that is small in size and one with two different domains and each of them has three nodes. It is observable that one-dimensional linear sensor network displays a common behavior.
Line topology with evenly distributed nodes
The purpose of this section is to explore single dimensional network arranged with base stations located at one place. The domains range between 2 and 4 and, therefore, in the case of two domains, the indices i,k and h take the numbers one and two and in the case of three domains they take the values between one ad four. All the strategies (two or three domains) display similar configuration. It is evident that as the number of nodes and size of network increases, the lifetime of the network increases too.
Line topology with randomly deployed nodes
In the section above section, the assumption was that the nodes are displayed at locations that have the same spacing between them. However, with randomly placed nodes, the problem constraints, and the design parameters remain the same. As in [Li and Mohapatra 2007], a noteworthy enhancement is provided by cooperation in the lifetime of the network. It is interesting to see that as the size of the network increases, the cooperation become larger. For example, when the cooperation is full, a network lifetime 3.04 times larger than that obtained when there are three non-cooperative networks with at least 12 nodes.
Two-dimensional disk-shaped sensor networks
Just like in one-dimension scenario, the first thing is to illustrate a characteristic traffic dispersal in a network with two dimensions and random nodes placement. Notably, this scenario is not as easy to come up with conclusions like in one-dimension cases. As in [Marla and M. Cardei 2009], there exists an irregular association between the number of separate streams for a sense node and how far it is from the base station. Data is sent directly when the nodes are very close to the BS. On the other hand, the nodes that are very far from the BS perform what is referred to as multi-hop communication, and the amount of data that requires being transmitted directly is very small.
Random placement of node and co-located sinks
Just like in section 3.1, the problem constraints remain unchanged. The expected spacing between nodes should be 100m like in one-dimensional deployment. For that reason, the area of the display is 2500 πm2 times the number of nodes. It is worth noting that this is the sections that determine and illustrates the advantages of cooperative forwarding over the non-cooperative one. The conclusion, therefore, is that full cooperation obtains a lifetime that is 106% higher than that obtained when using non-cooperative domains in a network that has 48 nodes.

Impacts of the node density
To be able to determine the effect that the density of the node has the lifetime improvement, the strategies of deploying the nodes and the problem constraints need to remain unchanged. The node density is changed by increasing the deployment area, and their total number remains the fixed. When the density value of the nodes is small, the network lifetime ration in a network with one or more domain is close to unity. It, therefore, becomes hard to justify the cooperation between the domain to prolong the lifetime of the network.
Comparing different cooperation strategies
This section involves the analysis and comparison of strategies for deployment of BS not to mention the strategies in the absence of communication between bases.
Results for different base station locations and inter-base station communication strategies
The first thing is the analysis of the effect of a BS location has on the lifetime of a network. The most important result obtained in this section is that when using four cooperating BSs in a location under monitoring, the lifetime of the network is 150% longer than when there are four BSs and each one represents a single domain.
Results with equal energy dissipation constraint
The results here show that two pairs of domains disperse equal amounts of energy for the purpose of forwarding data. Evidently, when the BSs are co-located, the lifetime may decrease because the energy corresponding constraint is important for smaller networks. However, with an increase in the number of nodes, they converge to a value that is insignificantly small.
Conclusion
As in [Nagata et al. 2012], the potential benefits of using a multiple-domain WSN paradigm have been extensively discussed. In fact, there is enough proof in the paper that this is the new concept that is being adopted within WSN research. Therefore, this work is very important because the performance numerous multiple-domain cooperation strategies have been evaluated. The project is also very significant in exploring the effects of cooperation and non-cooperation in a setting that is simple and with line networks that are well suited for this purpose. In this project, we have learned that in intra-cluster and inter-cluster communication, the sink’s closest nodes are essential to convey data from nodes that are very far and that can cause hotspot problem. We have also learned the major advantages associated with this co-operative communication which includes spatial multiplicity and data quantity.
In the project, I had the idea that cooperative communication is best suited for application in a small area. All I had to prove was that when the network’s area is increased, the distance between a node and the BS also increases thus causing the weighty encumbrance on the nodes that are closest to the BS. I understood that increased cost was a major pitfall of cooperative communication. The other two pitfalls, i.e. delayed-constraint application and network diversity were ideas borrowed from [Nagata et al. 2012]. Due to time limitation, ideas such as line topology with irregularly deployed nodes and impacts of node density were not discussed. With more time to work on this project, it would have been easier to determine how deploying nodes randomly increases the size of cooperation. Also, the project would have reflected on the impact that changing the density of the nodes has on aggregating the lifetime of cooperation while suing multi-domain.
References
[Li and Mohapatra 2007] “Analytical modeling and mitigation techniques for the energy hole problem in sensor networks,” Pervasive and Mobile Computing, Vol. 3, issue 3, pp. 233-254.
[Marla and M. Cardei 2009] “Improved sensor network lifetime with multiple mobile sinks, “Pervasive and Mobile Computing, Vol. 5, Issue 5, pp. 542-555.
[Nagata et al 2012] “A routing method for cooperative forwarding in multiple wireless sensor networks,” in Proc. 8th Int. Conf. Netw. Services (ICNS), Mar. pp. 43–46.
[Shamani et al, 2013] “Adaptive Energy Aware Cooperation Strategy in Heterogeneous Multi-Domain Sensor Networks, ” Procedia Computer Science, Vol. 19, pp. 1047-1052.