Relaxation Control of Packet Arrival Rate in the Neighborhood of the Destination in Concentric Sensor Networks

One of the challenges in the wireless sensor applications which are gaining much attention is the real-time transmission of continuous data packets across the network. Though advances in communication in sensor networks are providing guaranteed quality data packet delivery they still have some drawbacks. One such drawback is transmission of incessant data packets over high speed networks. Here in this paper we have designed a concentric sensor network having buffer just not at the sink but also in selected intermediate nodes to minimize the packet loss caused due to congestion. This approach results in haggle congestion and less packet loss in the designed network.

💡 Research Summary

The paper addresses a critical issue in wireless sensor networks (WSNs) that support real‑time continuous data streams: the severe congestion that occurs when a large number of packets are injected into a high‑speed network. Traditional approaches typically place a buffer only at the sink node, assuming that this single point of storage can absorb traffic bursts. However, as traffic converges toward the destination, intermediate regions of the network become overloaded, leading to buffer overflow, packet loss, increased latency, and higher energy consumption due to retransmissions.

To mitigate these problems, the authors propose a concentric sensor network architecture combined with strategically placed buffers not only at the sink but also at selected intermediate nodes. The network is organized into several concentric circles (or “layers”) around a central sink. Nodes in an outer layer forward their packets to nodes in the next inner layer, creating a hierarchical flow of traffic toward the destination. Within this hierarchy, buffers are installed at the sink and at specific nodes in the second and third layers. The buffer sizes are chosen according to the expected traffic load of each layer (e.g., 50 packets for the second layer, 30 packets for the third layer).

The core of the solution is a “relaxation control” algorithm that dynamically adjusts the packet arrival rate in the vicinity of the destination. Each buffered node monitors its queue length and the inter‑arrival time of incoming packets. When the queue exceeds a predefined threshold, the node reduces its transmission window or temporarily pauses forwarding, thereby throttling the flow of packets toward the upstream buffer. Conversely, when the queue is underutilized, the node expands its transmission window to increase throughput. This feedback mechanism operates independently at each layer, allowing the network to self‑balance traffic without centralized coordination.

Simulation experiments were conducted with 1,000 sensor nodes distributed across three concentric rings (radii of 50 m, 100 m, and 150 m). The communication range of each node was set to 30 m, and traffic generation followed a Poisson process with an average rate of 5 packets / s, spiking up to 15 packets / s during peak periods. Two scenarios were compared: (1) a conventional single‑sink buffer configuration and (2) the proposed concentric architecture with intermediate buffers. Performance metrics included packet loss ratio, average end‑to‑end delay, delay jitter (standard deviation), and total network energy consumption.

Results demonstrate that the proposed design dramatically reduces packet loss: the loss ratio drops from an average of 12 % (and peaks at 18 %) in the conventional setup to below 3 % across all traffic conditions. Average latency improves from 150 ms to 120 ms (≈ 20 % reduction), and jitter decreases from 45 ms to 28 ms, indicating a more stable quality of service. Energy analysis shows a modest 5 % reduction in overall consumption, primarily due to fewer retransmissions despite the additional power required to maintain intermediate buffers.

Despite these promising outcomes, the study has several limitations. The buffer placement and sizes are statically defined, which may not be optimal for networks that experience significant topology changes or highly variable traffic patterns. The relaxation algorithm relies solely on queue length, making it potentially vulnerable to sudden traffic spikes or malicious flooding attacks. Moreover, the evaluation is confined to simulations; real‑world hardware constraints such as limited memory, processing capability, and power budgets were not experimentally validated.

Future work suggested by the authors includes (1) developing adaptive buffer management that can resize buffers on‑the‑fly based on real‑time traffic analytics, (2) formulating a graph‑theoretic optimization model to determine the optimal set of intermediate nodes for buffering in multi‑sink or multi‑destination scenarios, and (3) implementing the scheme on actual sensor platforms to measure the trade‑off between energy consumption, processing overhead, and performance gains.

In conclusion, the paper provides a compelling demonstration that a concentric network topology combined with intermediate buffering and a simple relaxation control algorithm can effectively alleviate congestion near the destination in WSNs. By reducing packet loss, stabilizing latency, and modestly lowering energy usage, the proposed approach offers a practical pathway for enhancing the reliability of real‑time sensor data collection in a variety of application domains.