Predictive Congestion Control Protocol for Wireless Sensor Networks

Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks, result in a large number of packet drops, unfair scenarios and low throughputs with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, this paper presents a novel, decentralized, predictive congestion control (DPCC) for wireless sensor networks (WSN). The DPCC consists of an adaptive flow and adaptive back-off interval selection schemes that work in concert with energy efficient, distributed power control (DPC). The DPCC detects the onset of congestion using queue utilization and the embedded channel estimator algorithm in DPC that predicts the channel quality. Then, an adaptive flow control scheme selects suitable rate which is enforced by the newly proposed adaptive backoff interval selection scheme. An optional adaptive scheduling scheme updates weights associated with each packet to guarantee the weighted fairness during congestion. Closed-loop stability of the proposed hop-by-hop congestion control is demonstrated by using the Lyapunov-based approach. Simulation results show that the DPCC reduces congestion and improves performance over congestion detection and avoidance (CODA) [3] and IEEE 802.11 protocols

Route Aware Predictive Congestion Control Protocol for Wireless Sensor Networks

Congestion in wireless sensor networks (WSN) may lead to packet losses or delayed delivery of important information rendering the WSN-based monitoring or control system useless. In this paper a routing-aware predictive congestion control (RPCC) yet decentralized scheme for WSN is presented that uses a combination of a hop by hop congestion control mechanism to maintain desired level of buffer occupancy, and a dynamic routing scheme that works in concert with the congestion control mechanism to forward the packets through less congested nodes. The proposed adaptive approach restricts the incoming traffic thus preventing buffer overflow while maintaining the rate through an adaptive back-off interval selection scheme. In addition, the optimal routing scheme diverts traffic from congested nodes through alternative paths in order to balance the load in the network, alleviating congestion. This load balancing of the routes will even out the congestion level throughout the network thus increasing throughput and reducing end to end delay. Closed-loop stability of the proposed hop-by-hop congestion control is demonstrated by using the Lyapunov-based approach. Simulation results show that the proposed scheme results in reduced end-to-end delays

Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks

In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks

Comprehensive Survey Congestion Control Mechanisms in Wireless Sensor Networks:Comprehensive Survey

Wireless sensor network (WSN) occupies the top rank of the widely used networks for gathering different type of information from different averments. WSN has nodes with limited resources so congestion can cause a critical damage to such network where it limited resources can be exhausted. Many approaches has been proposed to deal with this problem. In this paper, different proposed algorithm for congestion detection, notification, mitigation and avoidance has been listed and discussed. These algorithms has been investigated by presenting its advantages and disadvantages. This paper provides a robust background for readers and researches for wireless sensor networks congestion control approaches. Keywords: WSN, Congestion Control, congestion mitigation, congestion detection, sink channel load, buffer load

A priority based routing protocol for wireless sensor networks

Recently, the demands on wireless sensor networks have switched from low traffic rate and static topology to more challenging requirements in order to meet the rapid expansion of WSN into various domain applications. This paper proposes a seamless cross layer solution that integrates network layer and medium access control to accommodate some of the new challenges. This new solution allows routing paths being generated dynamically to meet the requirement of potential mobile nodes. Higher data throughput and flow control are part of the new demands required to be addressed urgently. The proposed solution integrates a priority based MAC to handle congestion and packet loss problems which commonly happened in WSN when an occurrence of event spread into wide are

Adaptive-Compression Based Congestion Control Technique for Wireless Sensor Networks

Congestion in a wireless sensor network causes an increase in the amount of data loss and delays in data transmission. In this paper, we propose a new congestion control technique (ACT, Adaptive Compression-based congestion control Technique) based on an adaptive compression scheme for packet reduction in case of congestion. The compression techniques used in the ACT are Discrete Wavelet Transform (DWT), Adaptive Differential Pulse Code Modulation (ADPCM), and Run-Length Coding (RLC). The ACT first transforms the data from the time domain to the frequency domain, reduces the range of data by using ADPCM, and then reduces the number of packets with the help of RLC before transferring the data to the source node. It introduces the DWT for priority-based congestion control because the DWT classifies the data into four groups with different frequencies. The ACT assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a smaller number of packets for a data group with a low priority. In the relaying node, the ACT reduces the amount of packets by increasing the quantization step size of ADPCM in case of congestion. Moreover, in order to facilitate the back pressure, the queue is controlled adaptively according to the congestion state. We experimentally demonstrate that the ACT increases the network efficiency and guarantees fairness to sensor nodes, as compared with the existing methods. Moreover, it exhibits a very high ratio of the available data in the sink

MTADF: Multi Hop Traffic Aware Data For Warding For Congestion Control In Wireless Sensor Networks

In the past few years there is a remarkable change in the field of wireless sensor networks. Congestion occurs when there is a heavy traffic in the network. The heavy traffic in the network leads to wastage of energy and packet loss. Traffic Aware Dynamic Routing algorithm mitigates congestion by using one hop neighbor routing, hence throughput of the network is low. This paper proposed a Multi hop based Data Forwarding Technique to mitigate congestion. Queue length field and depth potential field play a major role to divert the traffic in the network to the alternate paths. The high traffic load leads to data queue overflow in the sensor nodes, these results in loss of important information about important events. Multi hop Traffic-Aware Dynamic Routing algorithm addresses congestion using depth potential field and queue length potential field. The algorithm forwards data packets around the congestion areas and scatters the excessive along multiple paths. The nodes with less load are efficiently utilized in response to congestion. The main aspect of the algorithm is to construct two independent potential fields using depth and queue length. Queue length field solves the traffic-aware problem. Depth field creates a backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential field to make dynamic decision for data forwarding. Simulations are conducted to evaluate the performance of our proposed algorithm and our proposed scheme performs better compared to previous work

A Survey on Multimedia-Based Cross-Layer Optimization in Visual Sensor Networks

Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by ad hoc error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks