Towards increasing spatial reuse, cellular networks may be augmented with ad hoc connectivity. In the resulting hybrid network, the coverage area of the base station (BS) is reduced and the users within this area relay packets from/to the users outside. With this approach, shorter range, higher-rate links are used; this favors an increase in spatial reuse and thus, the achievable capacity. However, multi-hop relaying overhead can hurt capacity. In this paper, we analytically compute the uplink capacity, defined as an upper bound on the achievable throughput under max-min fairness. To gage the tightness of the bound, we seek to find the optimal transmission schedule for delivering the packets from the nodes in a cell to the BS. In general, constructing the optimal uplink schedule is NP-hard. We develop a heuristic approach and show via simulations that the resulting performance is close to the derived capacity bound. Our results suggest that (a) the hybrid network can achieve up to a 200 % increase in the uplink capacity compared to a pure cellular network, and (b) the simulated throughput is close to the analytically computed capacity showing that our bound is tight.

In this paper, towards improving spatial reuse in a cellular network, we consider augmenting it with wireless ad hoc connectivity. The coverage area of each base-station is reduced and the users that are within the area relay traffic to nodes outside the area; these users further relay data to more distant users within the cell. The resulting network is referred to as a hybrid network. While this approach can result in shorter range higher-rate links and improved spatial reuse which, together favor a capacity increase, it relies on multi-hop forwarding which is detrimental to the overall capacity. Our objective in this work is to evaluate the impact of these conflicting factors on the capacity of the hybrid network and determine if this capacity is higher than that of the original cellular network. We formally define the capacity of the network as the maximum possible downlink throughput under the conditions of max-min fairness. We analytically compute the capacity of a two-dimensional hybrid network withregularplacements of base-stations (BSs) and users. We validate our analytical results via simulations. Our studies demonstrate that capacity improvements are possible in certain parametric regimes in which the penalty due to multi-hop relaying does not outweigh the gains due to spatial reuse and shorter higher-rate links. Our simulations also demonstrate that if the users are placed randomly, the behavioral results are similar to that with regular placements of users.

The accurate determination of the link quality is critical for ensuring that functionalities such as intelligent routing, load-balancing, power control and frequency selection operate efficiently. There are 4 primary metrics for capturing the quality of a wireless link: RSSI (Received Signal Strength Indication), SINR (Signal-to-Interference-plus-Noise Ratio), PDR (Packet-Delivery Ratio), and BER (Bit-Error Rate). In this paper, we perform a measurement-based study in order to answer the question: which is the appropriate metric to use, and under what conditions? We evaluate the relative accuracy of each metric by conducting experiments with multiple transmission rates and varying levels of interference on a large set of links. We observe that each metric has advantages and projects one or more limitations. Our study suggests that a careful consideration of these limitations is essential, and provides guidelines on the applicability of each metric.

We find that current group communications protocols are far from "one size fits all", they are typically geared toward and optimized for particular scenarios. Multicasting, in general, works well if the density of group members is sparse and in low mobility; broadcasting, in contrast, works well with a high density of group members and in high mobility. Due to the dynamics of the network, one strategy may be preferable to the other at different times and in different localized regions. In this paper, we first quantify the trade-offs between broadcasting and multicasting and evaluate the suitability of a strategy in various scenarios of deployment. Based on the lessons learned, we design a protocol that adapts in response to the dynamics of the network. We named our protocol Fireworks. Fireworks is a hybrid two-tier multicast/broadcast protocol that provides efficient and lightweight multicast dissemination and self-adapts in response to variations in the density and distribution of group members to provide efficient performance. Fireworks creates pockets of broadcast distribution in areas with many members, while it creates and maintains a multicast backbone to interconnect these dense pockets. Fireworks offers packet delivery statistics comparable to that of a pure multicast scheme but with significantly lower overheads. We also show that Fireworks has a lower level of degrading influence on the performance of coexisting unicast sessions than either traditional multicast or broadcast methods

In IEEE 802.11 based ad hoc networks, by simply manipulating the back-off timers and/or wait times prior to transmission, malicious nodes can cause a drastically reduced allocation of bandwidth to well-behaved nodes. This can result in causing bandwidth starvation and hence, a denial of service to legitimate nodes. We propose a combination of deterministic and statistical methods that facilitate detection of such misbehavior. With our approach, each of the nodes is made aware of the pseudo-random sequences that dictate the back-off times of all its one-hop neighbors. A blatant violation of the timer is thus, immediately detected. In certain cases, a node may be unable to monitor the activities of its neighbor and therefore deterministically ascertain if the neighbor is misbehaving. To cope with such cases, we propose a statistical inference method, wherein based on an auto-regressive moving average (ARMA) of observations of the system state, a node is able to estimate if its neighbor is indulging in misbehavior. Simulation results show that with our methods, it is possible to detect a malicious node with a probability close to one. Furthermore, the probability of false alarms is lower than 1%.