Over the last few years, wireless sensor networks have become increasingly common place in industries spanning agriculture, transportation, manufacturing, healthcare, and many others. With this growing usage has naturally followed a desire to improve and enhance the wireless sensor networks we’re steadily becoming more reliant upon.
Genetic algorithms are one method currently being developed and utilized to optimist wireless sensor networks and, in this article, we’ll be looking at how they are being used to address and enhance specific aspects of wireless sensors networks.
However, before we jump into that, let’s quickly go over a few common issues currently plaguing wireless sensor networks.
There are various issues that could affect wireless sensor networks. Some of the most common include power consumption, network lifespan, node placement, and clustering.
Power consumption within wireless sensor networks is of importance due to the widespread use of rechargeable batteries and other mobile energy sources to power sensors, meaning optimizing power usage is paramount.
Network lifespan is also an important consideration for businesses and enterprises deploying wireless sensor networks as they will, of course, want the most cost-effective and efficient systems available to them, making technologies that prolong network lifetimes highly desirable.
Node placement is another important factor in the deployment of wireless sensor networks and where nodes are placed will often depend on the expected application of the wireless sensor network and the size of the area for which it is intended to provide coverage. The task of node placement within wireless sensor networks is commonly known as localisation.
What are Genetic Algorithms?
Genetic algorithms or global heuristic algorithm, is a metaheuristic computational method, inspired by biological evolution, that aims to imitate the way biological organisms adapt as a part of their natural evolution in order to optimize different aspects of wireless sensor networks.
Genetic algorithms work by calculating the optimal solution to a problem by generating unique “individuals.” These individuals are then scored under the focused fitness function of the genetic algorithm in order to decide which traits are continued on through further generation.
Throughout the selection process, a new generation of individuals is created by mating members of the current generation on the basis of their fitness scores until an optimal solution is found. Within wireless sensor networks, the parameters determining a fitness score could include energy transfer and minimization, distance from a base station, and cluster placement.
While their use in optimization is becoming more and more widespread, genetic algorithms are also able to produce strong platforms for the introduction of technologies such as machine learning and artificial intelligence (AI) for use in classification and learning tasks that could take place alongside optimization objectives within a wireless sensor network.
Genetic algorithms have also been successfully used within areas such as aircraft manufacturing, drug design, software creation and telecommunications to name but a few.
Genetic Algorithms in Wireless Sensor Networks
Multimedia technologies such as video streaming and conferencing are already an everyday essential to the vast majority of businesses nowadays, and this has created the need for high-speed, efficient, and capable wireless communications systems and networks for optimal quality of service (QoS).
Using genetic algorithms, quality of service optimization can be achieved through what Abhishek Roy, Nilanjan Banerjee, and Sajal K. Das, authors of “An efficient multi-objective QoS-routing algorithm for wireless multicasting,” propose is a multi-objective genetic algorithm (MOGA).
With multi-objective genetic algorithms, QoS solutions are kept optimized within their own pool and multiple, unique solutions are offered that are contingent on the specific service requirements involved.
The process of assigning different applications to different radio frequencies is known as bandwidth allocation and can be a challenge for companies and organisations dealing with limited wireless resources.
Genetic algorithms can be used to utilize channel allocation efficiently by analyzing network bandwidth allocation using predefined QoS criteria for each multimedia variant. These variants are then assigned the most suitable quality of service level for their type while taking into account the availability of bandwidth resources within a network.
This approach allows genetic algorithms to attempt to produce optimal results in the distribution and allocation of bandwidth and resources.
As previously mentioned, the task of node placement within wireless sensor networks is commonly known as localisation and is one of the most important aspects to consider with regards to coverage, and locational QoS.
There are various different ways in which genetic algorithms can be used to improve the localisation process in wireless sensor networks. One way in which localisation could be optimized using genetic algorithms is through the use of anchor nodes, whereby each node locates its own position relative to three other “anchor” nodes.
This data can then be fed back to a base station where a centralized genetic algorithm could go to work as a post-optimizer, forming an accurate network map of node placements.