A two-step backoff scheme for improving the performance of the IEEE 802.11 distributed coordination function

This paper describes the binary exponential backoff mechanism of 802.11 distributed coordination function (DCF), and introduces some methods of modifying the backoff scheme. Then a novel backoff scheme, called Two-step Backoff scheme, is presented and illustrated. The simulation process in OPNET environment has been described also. At last, the analysis and simulation results show that the Two-step backoff scheme can enhance the performance of the IEEE 802.11 DCF.

5.2 GHz variable-gain amplifier and power amplifier driver for WLAN IEEE 802.11a transmitter front-end

A 5.2 GHz variable-gain amplifier (VGA) and a power amplifier (PA) driver are designed for WLAN IEEE 802.11a monolithic RFIC. The VGA and the PA driver are implemented in a 50 GHz 0.35 μm SiGe BiCMOS technology and occupy 1.12×1.25 mm~2 die area. The VGA with effective temperature compensation is controlled by 5 bits and has a gain range of 34 dB. The PA driver with tuned loads utilizes a differential input, single-ended output topology, and the tuned loads resonate at 5.2 GHz. The maximum overall gain of the VGA and the PA driver is 29 dB with the output third-order intercept point (OIP3) of 11 dBm. The gain drift over the temperature varying from -30 to 85℃ converges within±3 dB. The total current consumption is 45 mA under a 2.85 V power supply.

SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks

We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources-this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.

DIP: Density Inference Protocol for wireless sensor networks and its application to density-unbiased statistics

Wireless sensor networks have recently emerged as enablers of important applications such as environmental, chemical and nuclear sensing systems. Such applications have sophisticated spatial-temporal semantics that set them aside from traditional wireless networks. For example, the computation of temperature averaged over the sensor field must take into account local densities. This is crucial since otherwise the estimated average temperature can be biased by over-sampling areas where a lot more sensors exist. Thus, we envision that a fundamental service that a wireless sensor network should provide is that of estimating local densities. In this paper, we propose a lightweight probabilistic density inference protocol, we call DIP, which allows each sensor node to implicitly estimate its neighborhood size without the explicit exchange of node identifiers as in existing density discovery schemes. The theoretical basis of DIP is a probabilistic analysis which gives the relationship between the number of sensor nodes contending in the neighborhood of a node and the level of contention measured by that node. Extensive simulations confirm the premise of DIP: it can provide statistically reliable and accurate estimates of local density at a very low energy cost and constant running time. We demonstrate how applications could be built on top of our DIP-based service by computing density-unbiased statistics from estimated local densities.

On the Interaction between Data Aggregation and Topology Control in Wireless Sensor Networks

Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.