Next-generation Wireless Sensor Networks: Innovations in Data Quality, Data Aggregation and MAC Protocols, Edition 1

Technological development and improvements have accelerated the Sensor node design in terms of low power consumption, and low cost and also exhibit multifunctional and untethered communication in short distances. The capabilities of the sensor nodes like sensing, data collecting and processing, transferring, etc have assisted and greatly transformed the design, advancement and deployment strategies of Wireless Sensor Networks, where all the nodes collectively collaborate for the target application. The sensor nodes with the help of the sensors attached to them monitor various parameters and transmit the acquired data via wireless medium to a distant node which is called a sink or base station. The main function of the sensor network is to gather sensor data from the area/region of event occurrence and transmit it to the sink node. The nodes in the sensor network work in a collective manner, thus making it different from the ad-hoc networks.

A node is able to sense an event within a specified range. The strength of the event signal is also deterministic of which sensor nodes can sense the event. To ensure that no event is missed being reported, sensors in a WSN are densely deployed. The sensor node’s position is generally not fixed; the nodes might be randomly distributed around the phenomenon. So there is a need for protocols to have self-organizing capabilities, also the nodes must work in a co-operating manner to achieve effective information transfer. Sensor nodes have processing capabilities of their own; they locally process the data and then transfer it. The sensor nodes deployed in WSN have limited computational capacities, memory and power compared to ad-hoc networks. Sensor nodes in WSN may not have unique global identification as it can cause overhead in communication, also owing to a high density of sensor nodes, global identification can be challenging. In WSN there exist challenges in areas viz., scalability, cost, power, self-organization, interoperability, data compression, etc.

Sensor nodes aggregate the sensed data before transmitting it. Typically aggregation is performed at representative nodes or in the gateway nodes while the data packets are in transit to sink. Data aggregation protocols aim to remove redundant data, thus enhancing the lifetime of the sensor network. In a typical WSN, data is transmitted in a multi-hop fashion; nodes send data to their neighbors which are nearer to the sink. Nodes that are closely placed are likely to sense the same data and thus cause redundancy.

Based on the application requirement, sensors either transmit the data whenever an event is detected or periodically. These WSN characteristics and varied application areas motivate a sensor MAC which is operationally different from existing traditional MACs. Sensor networks MAC have node self-organization and energy conservation as their primary goal. The wireless channel access plays an important role in forwarding the data frames to the sink. Many MAC protocols are proposed for efficient channel access. WSN MAC techniques control and coordinate the radio component so that the network is energy efficient thereby improving lifetime considerably.

There are problems associated with the existing WSN systems. The existing schemes for data aggregation mechanisms are weakly related to data correlation and data redundancy, leading to poor data quality. The majority of the research work emphasizes that the clusterhead be elected based on node energy, which may not prove fruitful for data correlation-based aggregation. Data aggregation schemes employed in current techniques fail to perform data analysis cost-effectively. MAC schemes result in congestion in the nodes surrounding the base station, which should be eased to achieve better network performance.

To overcome these challenges we present a framework to enhance the data quality during the aggregation process. It is a novel and simple clustering algorithm that performs the selection of the clusterhead based on the data correlation factor. We also propose a novel hybrid MAC technique called Improved Funneling MAC for effective resource management. Both the protocols are implemented in MATLAB and simulation results are presented. Implementations are compared with the existing schemes and it is found that our implementations contribute to improved performance.