An Efficient Information Maximization Based Adaptive Congestion Control Scheme in Wireless Sensor Network

Abstract

Intelligent communication technology can enhance user experience and improve people's lifestyle. However, due to the limited resources of wireless sensor networks (WSNs), the congestion occurs when the node's traffic load exceeds it's the available capacity. Congestion may cause serious problems such as high packet loss and low throughput, which is an extremely deleterious impact on the performance of WSNs. To tackle this issue, a differentiated rate control data collection (DRCDC) scheme is proposed to avoid or alleviate the network congestion. Different from the previous scheme, the DRCDC scheme can mitigate the congestion by intelligently reducing the data that contains less information, and maintain the distortion rate of information collected by the network is small. The DRCDC mainly avoids or mitigates congestion on the spatial and temporal through information entropy theory, and at the same time makes the information collected by the network has the lowest distortion rate. 1) Congestion mitigation in spatial. Since there is a spatial correlation between sensing data, so in the case of collecting a certain amount of data, some missing data in space can be recovered from another spatially acquired data by matrix completion theory. Therefore, in the DRCDC scheme, when congestion occurs, the amount of data transmitted (forwarded) for nodes are congested will be reduced by an intelligent approach, thereby being able to mitigate congestion. 2) Congestion mitigation in temporal. when congestion occurs, reducing the data collection of time slots containing small information to reduce the flow rate that needs to be transmitted, thereby avoiding congestion while minimizing data distortion rate of the network. The experimental results of the DRCDC scheme in a planar network show better performance than the traditional data collection schemes and reducing the packet loss 2.8%-6.8% while reduce the maximum delay by 34.2%-76.3%.

Self-Adjustable Active Sequence to Reduce Latency in Duty Cycle WSNs

“…The rest of the N − t a 0,2 = 11 slots are divided into β = 4 segments. The first segment is [14,16] ∪ [1,2] and the length is ξ 1 = 5. The second segment is [4] and the length is ξ 2 = 1.…”

“…With the development of microprocessor technology, more and more intelligent sensing devices have been applied to various applications, which greatly expanding their application field [1]- [4]. Wireless sensor network (WSNs) are essential elements for realizing the Internet of Things (IoTs) [5]- [8], which can be widely used in industrial production sites [9], [10] traffic information [11], [12], crop monitoring [13]- [15], medical monitoring [16], [17], personal health monitoring of personal wearable devices and other applications [18], [19]. Wireless sensor nodes have been extensively developed due to their small size, low cost, and flexible deployment.…”

Adding Duty Cycle Only in Connected Dominating Sets for Energy Efficient and Fast Data Collection

“…Nowadays, With the development of microprocessor technology [1]- [3], the size of microprocessor becomes smaller and smaller, the processing speed and the storage space become larger and larger, and the reliability of device becomes higher and higher [4]- [6], which provides greater opportunities for the development of wireless sensor networks based on microprocessor [6]- [8]. Wireless sensor networks are now more widely used, including industrial fields [9], [10], environmental monitoring [11], [12], target tracking [13], [14] and traffic information monitoring [12], [15]- [17], etc., and due to the rapid development of sensor devices, the network architecture…”

Broadcast Based Code Dissemination Scheme for Duty Cycle Based Wireless Sensor Networks

“…Besides, the technology in the sensing devices has advanced rapidly, and it has a qualitative improvement in sensing the types of surrounding environment data and data sensitivity, range and other performance indicators [14]- [16], making a rapid development on wireless communication capability [17], [18], and greatly improving sensor-based applications [19]- [21]. Wireless sensor network can be widely applied to industrial production line monitoring [22]- [24], crop temperature, humidity perception [25], [26], traffic flow monitoring [27]- [29], geological hazard warning and monitoring [30]- [32], life monitoring, personal health monitoring [33], [34], location-based services [8], [35]- [37] and etc. Moreover, with the development of the sensing devices technology, various sensing devices are applied to different equipment, which not only enables the wireless sensor network to further expand its application domain and form a pervasive perceptual network, such as crowd source network [14], [15], [38], [39] and fog network [12], [42], [43] that are the significant components of the Internet of Things (IoT) [5], [20], [44]- [46], but also makes the current network architecture and computing model undergo a profound evolution [8], [47], [48], such as the conversion from the cloud computing model [9], [15], [31] to the edge computing model, from the central network to the evolution of the Edge network …”

“…In such networks, the time is divided into units of equal length called slot and then a work cycle can be composed of n slots. Each node in a work cycle merely selects one slot as a work slot to awake and sleep in other slots in order to save energy [52], [35].…”